1. Introduction

2. Materials and Methods

Table 1. Classes of HF in the study and the corresponding ICD-9 codes.

2.2. Feature Extraction

Feature extraction constitutes the procedure of uncovering meaningful patterns and insights within raw data, thereby crafting a more informative representation that refines the accuracy of prognosis and diagnosis [19]. In the realm of machine learning and data analysis, feature extraction revolves around the conversion of input data into a collection of features suitable for utilization as inputs in models or algorithms. In the present study, MATLAB software (version R2022b), designed and distributed by MathWorks (Natick, MA, USA), was employed for conducting the feature extraction process.

The feature extractor block is responsible for extracting three distinct categories of informative features from both PPG and ECG signals. The features extracted are broadly classified into three groups: The first category is centered on physiological parameters, including metrics such as Augmentation Index, Heart Rate, Arterial Stiffness Index, and Heart Rate Variability parameters (pNN50, NN50, RMSSD, SDNN), physiological features delve into parameters related to the body's physiological responses. The second group of features encompasses amplitude-related attributes, such as Pulse Pressure, Systolic pressure, Diastolic pressure, and P-wave characteristics, these features provide insights into the signal's strength and intensity. The third category involves interval-related features, including Peak to peak interval, QRS interval, and RR interval, the interval features offer information about the durations between specific points within the signal.

In accordance with the physiological underpinnings of heart failure and its relationship with ECG and PPG signals, we identified and extracted 13 pivotal features. These features encapsulate essential cardiovascular information and were extracted from both ECG and PPG signals within each cardiac cycle to facilitate heart failure evaluation. The extracted characteristics are represented visually in Figure 3, while Table 2 furnishes a comprehensive overview of each feature's class and the clinical importance/implications. It's essential to highlight that the PPG features extracted in this study are solely based on the identification of three readily discernible points, specifically the peak of the first derivative of PPG, foot and peak of the PPG waveform.

The morphological changes in cardiovascular features serve as indicators of structural or functional irregularities in the heart. In individuals with chronic heart failure (CHF) and acute myocardial infarction (AMI), heart rate variability in the time domain provides valuable prognostic information. Key parameters include the standard deviation of normal beat intervals (SDNN) and pNN50, representing the percentage of adjacent NN intervals differing by more than 50ms. A SDNN value of less than 50 ms or pNN50 lower than 3% is indicative of high risk, 50 to 100 suggests moderate risk, and a value over 100 ms or a pNN50 greater than 3% is considered normal [20]. The QRS interval, another predictor of heart failure, generally ranges between 0.06 – 0.12 ms in healthy individuals. A prolonged QRS interval may indicate delays in the ventricular depolarization process. The R-R interval, denoting the time between consecutive R waves in the QRS signal, is a critical parameter for assessing ventricular rate. In healthy individuals, normal ECG values for the R-R interval typically range between 0.6-1.2 seconds. Prolonged R-R intervals, defined as > 1.5 s, are commonly observed in patients with atrial fibrillation [21].

Pulse pressure, representing the difference between systolic and diastolic blood pressure, and Systolic pressure, an indicator of pulsatile changes in blood volume due to arterial blood flow, typically range between 0.5 – 10 mmHg and 80 – 120 mmHg, respectively, in healthy subjects. In heart failure, the arterial system undergoes changes, leading to increased stiffness. Elevated Augmentation Index values are indicative of increased wave reflections, reduced arterial compliance, and impaired vascular function, all of which are associated with heart failure. Structural and electrical changes in the heart can affect atrial function which in turn causes P-wave abnormalities, such as increased duration or altered shape, may signify atrial remodeling, a common feature in heart failure patients.

Heart rate (HR) also serves as a predictor of cardiovascular, cerebrovascular, and all-cause mortality [22]. A normal resting heart rate for adults ranges between 60 and 100 beats per minute. Increased heart rate has been associated with elevated cardiovascular risk and total mortality. The relationship between increased heart rate and adverse cardiovascular events remains significant even after adjusting for major cardiovascular risk factors, indicating the independent prognostic value of heart rate in various populations and clinical conditions.

Table 3. Features and the Normal Values for a Healthy Adult.

Table 3. Features and the Normal Values for a Healthy Adult.

Feature	Description	Duration	Disease Diagnosis
Pulse pressure	Difference between the systolic and diastolic blood pressure	0.5 - 10 mmHg	Atherosclerosis Congestive Heart failure
Systolic pressure	Indicator of the pulsatile changes in blood volume caused by arterial blood flow	80 – 120 mmHg	Artery stiffness. Heart Failure
P-wave	Atrial depolarization	0.08 – 0.11s	Heart Failure
Diastolic Pressure	Represents the amplitude of the signal during the diastolic phase of the cardiac cycle	<80 mm	Ischemic heart disease Cardiomyopathy
Peak to peak interval	Represents the duration between successive peaks in a signal	0.6 – 1.2s	Atrial fibrillation Heart failure
RR interval	The interval between two successive R-waves of the QRS complex ventricular rate	0.6 – 1.2s	Paroxysmal atrial fibrillation Congestive heart failure
Augmentation Index	The difference between systolic and diastolic blood pressure	20-80	Heart Failure
Heart Rate	A measure of the number of times the heart contracts or beats within a specific time frame, usually one minute	60 – 100 bpm	Heart Failure Atrial fibrillation
QRS interval	Ventricular depolarization	0.08 – 0.11s	Heart failure Tachycardia Acute Coronary Syndrome
RMSSD NN50 pNN50	Shows how active the parasympathetic system is relative to the sympathetic nervous system	19 – 48 ms 5 – 25ms 5% - 18%	Heart failure Hypertension Arrhythmia Coronary artery disease

2.4. Feature Importance Analysis and Feature Selection

The input feature vectors for both PPG and ECG were further reduced using the relief feature algorithm (ReliefF) [23,24]. The ReliefF algorithm is a filter-style feature selection technique that estimates weights by taking the nearest neighbor into account. In practical applications, this enhanced Relief derivative, referred to as ReliefF, is the most frequently utilized version [25]. The study employed the ReliefF algorithm to further reduce input feature vectors derived from both PPG and ECG signals. ReliefF is a well-established feature selection method known for its robust performance in multi-class classification scenarios and its capacity to handle noisy datasets with missing values. This improved variant, ReliefF, incorporates k nearest neighbors (KNN) from each class to estimate feature weights, enhancing the accuracy of weight estimation, particularly in noisy dataset settings [26]. The study initially used the ReliefF algorithm to assess the relevance of features within PPG and ECG signals separately. This analysis identified key features within each modality for heart failure assessment, aiding in the determination of which modality (ECG or PPG) offered better discriminatory power. However, this individual analysis had a limitation in that it might not capture interactions or synergies between PPG and ECG features. To overcome this limitation, the study conducted a combined feature importance analysis on a feature set that included both ECG and PPG data. This holistic approach provided a comprehensive view of feature importance, taking into account the contributions of both modalities. This comprehensive perspective offered insights into the significance of individual features within each signal and the collective impact of combining features from both PPG and ECG. Ultimately, this holistic view shed light on the roles of each modality and highlighted the potential advantages of integrating them in the context of heart failure evaluation.

The analysis of ECG data illuminates the pivotal role played by several key features as discriminators among different classes, particularly in individuals with heart failure. The heartbeat feature, reflecting the frequency of heartbeats, serves as a fundamental indicator of cardiac activity, with deviations signaling disruptions in pumping function. The RR interval, indicative of the time between successive R-peaks, offers insights into heart rate variability, highlighting irregularities in cardiac rhythm. The QRS interval, representing ventricular depolarization duration, provides information on the heart's electrical conduction system. Features such as RMSSD, SDNN, and pNN50, which gauge short-term variability, overall variability, and the percentage of significant variations, respectively, offer crucial information on autonomic function and cardiovascular regulatory mechanisms. Altered patterns in these features among individuals with heart failure contribute to a comprehensive understanding of the physiological changes associated with the condition. The examination of feature importance derived from PPG signals accentuates the clinical relevance of Systolic Pressure, Diastolic Pressure, Peak-to-Peak Interval, and pulse pressure in the PPG waveform for heart failure assessment. These features bear intricate connections to heart function. In the realm of heart failure, where cardiac function is frequently compromised, alterations in the morphology of these features serve as indicative markers of the physiological changes associated with the condition. Systolic pressure, representing the maximum arterial pressure during systole, and Diastolic pressure, indicating the minimum arterial pressure during diastole, offer a comprehensive view of blood pressure dynamics. The Peak to Peak Interval captures variations between consecutive peaks, reflecting the pulsatile nature of blood flow. Pulse Pressure, as a fundamental feature in the PPG waveform, provides information about the strength and regularity of the pulsatile signal. Comprehending the subtle variations in these characteristics greatly enhances the clinical applicability of PPG-based assessments by contributing to the nuanced assessment of heart failure. The above selected features for PPG (4 features) and ECG (6 features) were used for classifications involving the analysis of independent signals.

Figure 4. Feature Importance Score for ECG and PPG signal using ReliefF algorithm.

Figure 4. Feature Importance Score for ECG and PPG signal using ReliefF algorithm.

The feature selection process for the integration of PPG and ECG signals involved a meticulous examination of absolute values to identify features of substantial significance in the context of heart failure assessment. From the initial pool of extracted features, a refined set of ten (10) features was selected. Notably, Systolic Pressure, Diastolic Pressure, Peak to Peak Interval, NN50, pNN50, P-wave, Heart Rate, QRS Complex, RR Interval, Pulse Pressure, and Augmentation Index emerged as pivotal contributors to the classification task. The consideration of absolute values was paramount, ensuring a comprehensive evaluation of these features and capturing the essential dynamics of the cardiovascular system. These selected features exhibit notable clinical relevance, aligning with established physiological indicators of heart failure. ReliefF played a key role in highlighting their importance, emphasizing their ability to distinguish patterns associated with heart failure. The chosen features contribute to the overall effectiveness and clinical relevance of the heart failure assessment model, enhancing its interpretability and accuracy in classifying instances of the condition.

Figure 5. Importance score for Combined Features from PPG and ECG Signals.

Figure 5. Importance score for Combined Features from PPG and ECG Signals.

2.5. Data Partitioning and Classical Machine learning

Data partitioning is a crucial step in supervised machine learning, aiding in the training, optimization, and validation of predictive models. Various techniques exist for partitioning datasets into subsets, each suited to different dataset sizes. In this study, the datasets (n = 1636) was split into 75% (1227) train and 25% (409) test sets. The intentional design of the control group with a larger sample size aimed to provide a more balanced representation of the real-world distribution, enhancing the reliability and accuracy of our analysis. To ensure the model's stability and generalization, a 10-fold cross-validation (CV) procedure was applied to the training data before model optimization. During this process, the training data was divided into ten equal-sized 'folds.' The model was trained and validated ten times, with each fold taking turns as the validation set while the others were used for training. By evaluating the model's performance across different training data subsets, this method helped prevent overfitting. Also an extensive hyper-parameter tuning to optimize the model's performance while guarding against overfitting and under-fitting was done. The utilization of cross-validation strategy mentioned earlier and ensemble methods further contributed to the robustness of our model. Our commitment to avoiding over-parametrization was manifested in the comprehensive evaluation of various training aspects, emphasizing a balanced trade-off between model complexity and generalization. This meticulous approach allows for a fair and reliable comparison between the integrative and single-input models. The performance of the resulting model was evaluated on a different test set, giving an assessment of its generalization to completely unknown data.

In this study, Nine (9) machine learning algorithms were implemented for classification and performance analysis using the Weka software. Weka (Waikato Environment for Knowledge Analysis) is a cross-platform open source, renowned for its popularity in the realm of machine learning. Developed by the University of Waikato, this Java-based software offers a versatile platform for various data analysis and machine learning tasks [27].

• Random Forest (RF): Random Forest (RF) stands out as a well-known nonparametric tree-based supervised machine learning method, proficient in handling classification and regression tasks [28]. RF algorithms create numerous machine learning models and consolidate their results to arrive at more robust decisions or estimations than what individual models could achieve in isolation [28]. In comparison to various other machine learning techniques, RF offers distinct advantages. The base estimators within a random forest are trained independently, reducing the training process required for these models [29].
• Adaptive Boost (Adaboost): AdaBoost represents yet another powerful ensemble learning technique applicable to both classification [30]. This appraoch operates by amalgamating multiple weak learners, which individually perform sub-optimally, into a robust learner [31]. AdaBoost is particularly suitable for scenarios with a large number of features, efficiently selecting the most informative ones. Additionally, it effectively addresses class imbalance by adjusting training sample weights, ensuring fair attention to both positive and negative cases during training.
• Naïve Bayes: Naïve Bayes classifiers employ Bayes' probability theorem for data classification. They make an assumption that all features are independent of each other, even though this assumption is simplified, which is why they are referred to as 'naïve.' Bayes' rule calculates the probability of an event based on its relationship with another variable, with a basic representation as follows:

  $$\mathrm{P}(\mathrm{Ci}|\mathrm{x})=\frac{\mathrm{P}\left(\mathrm{x}|\mathrm{C}\mathrm{i}\right)\mathrm{P}\left(\mathrm{C}\mathrm{i}\right)}{P\left(x\right)},P\left(x\right)\ne 0$$

  (2)

  Naïve Bayes is computationally efficient, suitable for large datasets and real-time applications.
• Decision Tree (DT): Decision Trees are an intuitive, tree-like structured, non-parametric approach used for classification. To prevent overfitting, Decision Trees use pruning techniques to simplify the tree structure. They accommodate both categorical and numerical features, necessitating minimal data preprocessing and displaying proficiency in handling missing values. This robustness in the face of missing data is especially valuable for real-world situations where data incompleteness and noise are common.
• Support Vector Machine (SVM): SVM accomplishes classification by identifying the optimal hyperplane that improves the gap between classes [32]. In the case of combining non-invasive signals like PPG and ECG, the resulting feature space can be complex and high-dimensional. SVM can handle such data by finding an optimal hyperplane that maximally separates the different classes, even in high-dimensional feature spaces. This capability allows SVM to capture complex relationships and patterns in the data, which can be crucial for accurately classifying heart failure patients.
• K-Nearest Neighbors (KNN): KNN classifies data points by taking a vote based on the class types of their k nearest neighbors in multi-dimensional space. This localized approach is advantageous in scenarios where nearby data points hold significant influence. In the context of heart failure evaluation, KNN can uncover subtle patterns in PPG and ECG signals, aiding in diagnosis. In its basic form, KNN assigns a class depending on the largest class within the k nearest neighbors.
• Multilayer Perceptron (MLP): MLPs are a class of feedforward neural networks [33]. MLPs excel at modeling complex, nonlinear data relationships but require careful design of architecture, including hidden layer count, neuron numbers, and activation functions for optimal performance. It can be applied in a range of domains including image and speech recognition, natural language processing, and medical diagnosis, showcasing their versatility in machine learning [34,35].
• Random Tree (RT): In practice, Random Tree algorithms offer a good balance between simplicity and performance and are a valuable tool for various machine learning tasks [28]. The Random Tree algorithm is built on the foundation of decision trees, which are known for their ability to recursively partition data into subsets based on feature values. However, Random Trees introduce an element of randomness into the decision tree construction process through random feature selection and Bootstrap Aggregating (Bagging).
• Bayesian Net (BayesNet): Bayesian Network is a probabilistic graphical model widely used for various machine learning tasks, especially in fields like healthcare, finance, and natural language processing. It's based on Bayesian probability theory and graph theory, offering a compact and intuitive way to represent complex probabilistic relationships among variables. The effectiveness of BayesNets in managing uncertainty is one of its main advantages.BayesNets offer interpretable results, allowing you to understand how variables influence each other. This is crucial for decision-making in sensitive areas like healthcare.

Each machine learning algorithms mentioned above were implemented in this study based on its specific strengths and suitability for the study's goals, including handling complex features, dealing with noisy data, and providing insights into feature importance or relationships within the dataset, also the combination of these diverse algorithms allows for a comprehensive evaluation of heart failure using the extracted PPG and ECG features. The Table below gives tabular representation of this selection rationale

Table 4. Summary of Machine Learning Algorithms and their Selection Rationale.

Table 4. Summary of Machine Learning Algorithms and their Selection Rationale.

Algorithm	Strength’s	Reasons for selection
SVM (Support Vector Machine)	• Effective in high-dimensional spaces • Works well with complex datasets • Good generalization capabilities	Chosen for its ability to handle complex feature spaces and its effectiveness in classification tasks. [36,37]
Random Forest	• High predictive accuracy • Handles both numerical and categorical data • Reduces overfitting	Selected for its robustness and ability to deal with noisy data, which is common in medical datasets [28,29].
K-nearest Neighbor (KNN)	• Simple and intuitive • Non-parametric and adaptable	Employed for its simplicity and adaptability in classifying data points based on their proximity to neighbors [38,39,40].
Random Tree	• Ensemble method combining decision trees • Resistant to overfitting • Handles mixed data types	Utilized for its robustness and versatility in handling various data types and potential for accurate classification and can handle missing values efficiently [41,42,43].
AdaBoost	• Sequential ensemble learning • Combines weak learners for a stronger model • Good at handling imbalanced data	Chosen for its ability to improve model accuracy by sequentially learning from previous models' mistakes [30,43].
BayesNet (Bayesian Network)	• Probabilistic graphical model • Good for modeling dependencies among variables	Employed for its ability to model complex relationships and dependencies between features in medical data [44,45].
Decision Tree	• Intuitive and easy to understand • Interpretability	Selected for its simplicity and interpretability, making it useful for gaining insights into feature importance [46,47].
NaiveBayes	• Simple and computationally efficient • Performs well with limited data	Chosen for its efficiency in handling datasets with limited samples and has a short computational data training time [48,49].
MLP (Multilayer Perceptron)	• Deep learning architecture • Can model complex non-linear relationships • Suitable for large datasets	Adopted because of its deep architecture, which enables it to recognize complex connections and trends in the data [50,51].

3. Results and Discussion

3.1. Result from Classification with Features Extracted from Single PPG Signal

The features extracted from the PPG signals were employed to compute the performance metrics using the various machine learning algorithms discussed in the previous section. PPG signals inherently capture dynamic alterations in blood volume and vascular attributes, thus providing distinctive insights into cardiovascular health from an alternative vantage point. The outcomes of this approach were highly promising, as evident below.

Figure 6. Heart failure (HF) classification performance (Specificity, Sensitivity, accuracy, precision, AUC and F1-score) with features extracted from single PPG signal.

Figure 6. Heart failure (HF) classification performance (Specificity, Sensitivity, accuracy, precision, AUC and F1-score) with features extracted from single PPG signal.

From the figure above, Random Forest stands out as the top performer with an impressive accuracy of 97.10%, sensitivity of 97.05%, specificity of 96.88%, precision of 96.28%, AUC value of 97.20% and F1-score of 96.66% indicating its strong ability to correctly classify individuals with and without heart failure. It also excels in sensitivity, specificity, precision, and AUC, demonstrating its comprehensive effectiveness in evaluating heart failure. While Random Forest takes the lead, it's worth noting that other models, such as Support Vector Machine (SVM), K-Nearest Neighbor, and Decision Tree, also deliver commendable performances with accuracies ranging from 93% to 96%. However, Random Forest consistently outperforms these alternatives in most performance metrics, reinforcing its position as the optimal choice for heart failure classification.

3.4. Comparison OF Results Obtained

Performance metrics obtained from this novel approach of the integration of the PPG and ECG signal was also tested and compared to the results gotten from the performance of PPG and ECG signals independently. Figure 6 and Figure 7 illustrate the performance results obtained from the classification performed on these signals independently.

In comparison with the performance result obtained from the integration of these features, it is evident that the integration of these signals for heart failure study provides a unique insights into cardiovascular health from an alternative vantage point.

Figure 8. Classification performance (accuracy, precision, sensitivity, specificity, f1-score and AUC) of various machine learning algorithms on single signals and on integrated features from signals.

Figure 8. Classification performance (accuracy, precision, sensitivity, specificity, f1-score and AUC) of various machine learning algorithms on single signals and on integrated features from signals.

The table above and figure demonstrates the comparison between the results obtained from the analysis of the integration of PPG and ECG signals versus the results obtained from the ECG and PPG signals in isolation. When PPG data was considered in isolation, the Random Forest model emerged as the top performer, achieving an accuracy of 97.10%. This model demonstrated remarkable sensitivity, specificity, precision, and an F1-Score, all hovering around the 96-97% range. These metrics collectively indicated its strong potential for accurate heart failure classification.

In contrast, the ECG data was effectively evaluated using the MLP (Multilayer Perceptron) model, yielding an accuracy of 96.40%. While its accuracy was slightly lower than that of PPG, MLP exhibited a well-balanced trade-off between sensitivity and specificity, making it a valuable contender in heart failure assessment.

It can also be noted that the result obtained from using the PPG signal outperformed the result from using only the ECG signals, this can be attributed to some factors such as PPG signals being less susceptible to interference from electrical sources and electronic equipment compared to ECG, This characteristic ensures that PPG measurements remain reliable and consistent in various environments, this is particularly beneficial in settings where electrical interference may be present, allowing for dependable monitoring and accurate assessment of heart failure without external disruptions. Also, PPG excels in evaluating peripheral hemodynamics, offering valuable insights into blood circulation beyond the heart, it effectively measures changes in blood volume in peripheral blood vessels, shedding light on the efficiency of circulation and peripheral perfusion. Given that HF often affects peripheral blood flow, this capability is pivotal for understanding the broader cardiovascular dynamics associated with the condition. Also, in terms of motion tolerance, PPG's resilience to motion artifacts is a notable advantage, particularly for individuals with heart failure. Patients with HF may experience limited mobility or discomfort, and PPG's ability to maintain measurement accuracy even during subtle movements ensures that vital data can be reliably collected. This motion tolerance enables continuous monitoring without undue disruption, a crucial aspect in assessing HF patients' condition.

However, the most notable findings arose from the integration of both PPG and ECG signals, where the Random Forest model demonstrated exceptional performance. This integrated approach resulted across various evaluation metrics, including accuracy (98.00%) sensitivity (97.60%), specificity (96.90%), precision (97.20%), AUC (97.70%), and F1-Score (98.40%), suggesting that the combination of these signals significantly enhanced the model's capability for heart failure detection. Moreover, an outstanding AUC of 97.70%, indicates a superior ability to discern heart failure cases while minimizing false positives.

These outcomes highlight the potential of integrating PPG and ECG data for more accurate heart failure assessment. These results reveals superior performance compared to the use of these signals independently. Particularly, when assessed using the Random Forest model, the integrated approach exhibited exceptional accuracy and overall effectiveness, highlighting its potential significance in heart failure evaluation.

3.5. Comparison with Other Works

The performance of the proposed approach was also assessed in comparison to prior studies that independently employed ECG and PPG for various applications. Our findings indicate that the proposed method exhibited superior performance when contrasted with these studies, which separately utilized ECG and PPG modalities for their distinct analyses. The results of this comparative analysis are detailed in Table 6 below.

From the result below (Table 7), In comparison to previous ECG-focused studies, the current study achieved a high performance metrics ranging from 96.40%, 96.70%, 96.00%, 95.130% and 95.90%. Notably, the Multi-layer Perceptron (MLP) model in the current study outperformed the majority of previous ECG-based studies in terms of accuracy, sensitivity, Specificity, and precision.

For PPG analysis, the current study achieved high accuracy, Specificity, and Sensitivity, with values ranging from 96.88% to 97.10%. These results outperform several previous PPG-focused studies in terms of accuracy, sensitivity and specificity. The Random Forest (RF) model stood out with the highest accuracy.

The integration of PPG and ECG signals in the current study demonstrated promising results, with accuracy ranging from 91.20% to 98.00%. These results surpassed the majority of both ECG and PPG-focused studies, indicating that the combination of these signals provides a substantial benefit in heart failure assessment. The Random Forest (RF) model achieved the highest accuracy in this integrated approach, further emphasizing its effectiveness in comprehensive heart failure evaluation.

Table 7. Comparison of Results obtained with existing Literature.

Table 7. Comparison of Results obtained with existing Literature.

Author		Dataset	Signal	Features extracted	Algorithm		Acc. (%)	Sens. (%)	Spec (%)	Pre. (%)	F1Score
Simge et al. [52]		UCI 300	ECG	Chol, trestbps, fbs, restecg, slope	Cubic SVM Linear SVM DT Ensemble		52.3 67.3 67.7 67.0	-	-	-	-
Ali et al. [53]		UCI	ECG	RestECG, Trestbps, Chol, fbs	KNN SVM NaïveBayes		80 83 84	-	75 77 80	80 82 83	-
Shouman et al [54]		CHDD	ECG	Chol, trestbps, fbs, restecg	GRDT NaïveBayes KNN		79.1 83.5 83.2	75.6 78.0 76.7	81.6 80.8 85.1	- - -	- - -
Tu et al. [55]		CI	ECG	Chol, trestbps, fbs, restecg	Bagging DT		81.41 78.91	74.93 72.01	86.64 84.48	- -	- -
Bashir et al. [56]		CHDD 303	ECG	Chol, trestbps, fbs, restecg	Ensemble NaïveBayes DT SVM		81.82 78.79 76.57 86.67	73.68 68.49 63.58 73.68	92.86 92.86 71.24 79.51	- - - -	82.17 73.61 71.51 65.10
Pal et al. [13]		50	PPG	Crest-time, Augmentation index, pulse pressure, SA/DA	BT SVM KNN LR		94 85 83 83	95 83 79 83	5 87 82 85	97 83 97 82	96 87 89 85
Banerjee et al. [57]		MIMIC II 112	PPG	Systolic peak,NN-interval,HRV	SVM		-	82	88	-	-
Paradhker et al. [58]	MIMIC II 55		PPG	Augmentation index, stiffness index	SVM		-	85	78	-	-
Current Study	MIMIC III 1636		ECG	QRS interval, RR-Interval, HRV, Heart Rate, P-wave	MLP	96.40		96.70	96.00	95.30	95.90
			PPG	S.P, NN-interval, D.A, P.A, A.I	RF	97.10		97.05	96.88	91.20	96.66
			Integration of PPG and ECG signal		SVM	98.00		7.60	96.90	97.20	97.70

Note: Acc.: Accuracy, Spec.: Specificity, Sens.: Sensitivity, Pre.: Precision, KNN.: K-nearest Neighbor, BT.: Boosted Tree, SVM: Support Vector Machine, MLP.: Multi-layer Perceptron, LR.: Logistic Regression, DT.: Decision Tree, RF.: Random Forest, RT.: Random Tree. GRDT.: Gain ratio decision tree, CHDD.: Cleveland Heart Disease Dataset. S.A; Systolic Pressure, D.P; Diastolic Pressure, P-to-P; peak to peak amplitude, P.P: Pulse Pressure.

4. Limitations of the Study

5. Clinical Application Prospect, Future Work and Conclusion

References

1. Groenewegen, A.; Rutten, F.; Mosterd, A.; Hoes, A. Epidemiology of heart failure. European Journal of Heart Failure 2020, 22, 1342–1356. [Google Scholar] [CrossRef] [PubMed]
2. An B, Shin J, Kim S, et al. Smart Sensor Systems for Wearable Electronic Devices. Polymers (Basel) 2017, 9, 1–41. [Google Scholar]
3. Roger, Veronique L. Epidemiology of heart failure. Circulation Research 2013, 113, 646–659. [Google Scholar] [CrossRef]
4. Go A, Mozaffarian D, Roger V, et al. American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics--2013 update: a report from the American Heart Association. Circulation 2013, 127, e6–e245. [Google Scholar]
5. Yancy C, Jessup M, Bozkurt B, et al. Guideline for the management of heart failure. A report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Journal of the American College of Cardiology 2013, 62, e147–e239. [Google Scholar]
6. Heart Failure by Maheedhar Gedela, MD; Muhammad Khan, MD; and Orvar Jonsson, MD. https://www.researchgate.net/publication/283899687.
7. Pielmuş A, Osterland D, Klum, M, et al. Correlation of arterial blood pressure to synchronous piezo, impedance and photoplethysmographic signal features. Current Directions in Biomedical Engineering 2017, 3, 749–753. [Google Scholar] [CrossRef]
8. Bruno R, Duranti E, Ippolito C, et al. Different Impact of Essential Hypertension on Structural and Functional Age-Related Vascular Changes. Hypertension 2017, 69, 71–78. [Google Scholar] [CrossRef]
9. Allen, J. Photoplethysmography and its application in clinical physiological measurement. Physiol Measur 2007, 28, R1. Available online: http://stacks.iop.org/0967-3334/28/i=3/a=R01. [CrossRef]
10. Banerjee R, Vempada R, Mandana K, et al. Identifying coronary artery disease from photoplethysmogram. ACM International Joint Conference 2016, 1084–1088. [Google Scholar] [CrossRef]
11. Vo K, Kasaeyan N, Emad N, Amir J, et al., ECG waveform synthesis from PPG with conditional wasserstein generative adversarial networks. 2021, 1030-1036. [CrossRef]
12. Paradkar, N.; Chowdhury, S. Coronary artery disease detection using photoplethysmography. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2017, 2017, 100–103. [Google Scholar] [CrossRef]
13. Pal P, Ghosh S, Chattopadhyay B, et al. Screening of Ischemic Heart Disease based on PPG Signals using Machine Learning Techniques [C]. 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada. 2020, 2020, 5980–5983. [CrossRef]
14. Bashir, S.; Usman, Q.; Farhan, H. A Multicriteria Weighted Vote Based Classifier Ensemble for Heart Disease Prediction. Computational Intelligence 2016, 32, 615–645. [Google Scholar] [CrossRef]
15. Tian, X.; Zhu, Q.; Li, Y.; Wu, M. Cross-Domain Joint Dictionary Learning for ECG Reconstruction from PPG.2020. 936-940. [CrossRef]
16. Chen A, Huang S, Hong P, Cheng C, Lin E. HDPS: heart disease prediction system. In Computing in Cardiology. IEEE: Hangzhou, 2022. pp. 557–560.
17. Kamath, C. A new approach to detect congestive heart failure using detrended fluctuation analysis of electrocardiogram signals. J Eng Sci Technol 2015, 10, 145–59. [Google Scholar] [CrossRef]
18. Moody B, Moody G, Villarroel M, Clifford G, Silva I. MIMIC-III Waveform Database (version 1.0). PhysioNet, 2020. [CrossRef]
19. https://www.mathworks.com/discovery/feature-extraction. 2: (Accessed, 5 February 2024.
20. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Circulation 1996, 93, 1043–1065. [Google Scholar] [CrossRef]
21. Xu H, Li J, Zhong G, et al. Characteristics of the Dynamic Electrocardiogram in the Elderly with Nonvalvular Atrial Fibrillation Combined with Long R-R Intervals. Evidence Based Complement Alternate Medicine 2021, 2021, 4485618. [Google Scholar] [CrossRef]
22. Woodward M, Webster R, Murakami Y, et al. The association between resting heart rate, cardiovascular disease and mortality: evidence from 112,680 men and women in 12 cohorts. European Journal of Preventive Cardiology 2014, 21, 719–726. [Google Scholar] [CrossRef]
23. Kira, K.; Rendell, L. The feature selection problem: Traditional methods and a new algorithm. In: AAAI, 1992, 129‐134. [CrossRef]
24. Kira, K.; Rendell, L. A practical approach to feature selection. In: Proceedings of the ninth international workshop on machine learning (ML92) 1992, 249-256.
25. Urbanowicz, R.; Meeker, M.; Cava, W.; Olson, R.; Moore, J. Relief-based feature selection: Introduction and review. Journal of Biomedical Informatics 2018, 85, 189–203. [Google Scholar] [CrossRef]
26. Kononenko, I. Estimating attributes: Analysis and extensions of RELIEF. European Conference on Machine Learning 1994, 784, 171–182. [Google Scholar] [CrossRef]
27. Witten, I.; Frank, E. Data Mining: Practical machine learning tools and techniques, 2nd Edition, Morgan Kaufmann, San Francisco, 2005.
28. Leo Breiman. Random forests. Machine learning 2001, 45, 5–32. [Google Scholar] [CrossRef]
29. Liaw, A.; Wiener, M. Classification and regression by random Forest. R News 2001, 2, 18–22. [Google Scholar]
30. Freund, Y.; Schapire, R. Experiments with a new boosting algorithm. In ICML, 1996, 2, 148–156. [Google Scholar]
31. Freund Y, Schapire R, Abe N. A short introduction to boosting. Journal of Japan Soceity for Artificial Intelligence 1999, 14, 1612. [Google Scholar]
32. Corinna, C.; Vapnik, V. Support-vector networks. Journal of Machine Learning 1995, 20, 273–297. [Google Scholar]
33. Kurt H, Maxwell S, Halbert White. Multilayer feedforward networks are universal approximators. Neural networks 1989, 2, 359–366. [Google Scholar] [CrossRef]
34. Bishop, C. Neural networks for pattern recognition. Oxford university press 1995, 1, 145–164. [Google Scholar]
35. Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. MIT Press Cambridge 2016, 8, 170–224. [Google Scholar]
36. Yinglin, X. Correlation and association analyses in microbiome study integrating multiomics in health and disease, Editor(s): Jun Sun, Progress in Molecular Biology and Translational Science, Academic Press 2016, (171): 309 – 491. [CrossRef]
37. Bhavsar, H.; Ganatra, A. Comparative Study of Training Algorithms for Supervised Machine Learning. International Journal of Soft Computing and Engineering 2012, 2, 2231–2307. [Google Scholar]
38. An, Q.; Rahman, S.; Zhou, J.; Kang, J. A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges. Sensors. 2023, 23, 4178. [Google Scholar] [CrossRef]
39. Duneja, A.; Puyalnithi, T. Enhancing classification accuracy of k-nearest neighbors algorithm using gain ratio. Int. Res. J. Eng. Technol 2017, 4, 1385–1388. [Google Scholar]
40. Shouman, M.; Turner, T.; Stocker, R. Applying k-nearest neighbour in diagnosing heart disease patients. Int. J. Inf. Educ. Technol. 2012, 2, 220–223. [Google Scholar] [CrossRef]
41. Kulkarni, V.; Sinha, P. Efficient Learning of Random Forest Classifier using Disjoint Partitioning Approach. Proceedings of the World Congress on Engineering 2013, 20132, 1–5. [Google Scholar]
42. Ying, Mi. Imbalanced Classification Based on Active Learning SMOTE, Research Journal of Applied Sciences, Engineering and Technology 2013, 5, 944-949.
43. More, A.; Rana, S.; Dipti, P. Review of random forest classification techniques to resolve data imbalance. 72–78. [CrossRef]
44. Friedman N, Geiger D, Goldszmidt Moises. Bayesian Network Classifiers. Machine Learning 2003, 29, 131–163. [Google Scholar] [CrossRef]
45. Uusitalo Laura. Advantages and challenges of Bayesian networks in environmental modeling. Ecological Modelling 2006, 203, 312–318. [Google Scholar] [CrossRef]
46. Song, Y.; Lu, Y. Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry 2015, 27, 130–135. [Google Scholar] [CrossRef] [PubMed]
47. Vijay, K.; Bala, D. Data Science (Second Edition), Morgan Kaufmann 2019, Pages 65-163, ISBN 9780128147610. [CrossRef]
48. De S, Gilberto F, et al. Engineering Systemsʹ Fault Diagnosis Methods. Reliability Analysis and Asset Management of Engineering Systems. 2021; 165-187, Accessed 16 Dec. 2023. [CrossRef]
49. Dulhare, U. Prediction system for heart disease using Naive Bayes and particle swarm optimization. Biomed. Res. 2018, 29, 2646–2649. [Google Scholar] [CrossRef]
50. Akkaya B, Çolakoğlu N Comparison of Multi-class Classification Algorithms on Early Diagnosis of Heart Diseases. 2019, 3, 261 -311.
51. Bikku, T. Multi-layered deep learning perceptron approach for health risk prediction. J Big Data 2020, 7, 50. [Google Scholar] [CrossRef]
52. Ekiz, S.; Pakize, E. Comparative study of heart disease classification. 2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT) (2017): 1 N/A4.
53. Nassif, A.; Mahdi, O.; Nasir, Q.; Talib, M.; Azzeh, M. Machine learning classifications of coronary artery disease. In Proceedings of the 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI N/ANLP), Pattaya, Thailand, 15–18 November 2018; IEEE: New York, NY, USA. pp. 1–6. [Google Scholar]
54. Shouman, M.; Turner, T.; Stocker, R. Integrating Naive Bayes and K-means clustering with different initial centroid selection methods in the diagnosis of heart disease patients. Computer Science and Information Technology.
55. Chau, T.; Dongil, S.; Dongkyoo, S. Effective Diagnosis of Heart Disease through Bagging Approach. IEEE 2nd International Conference on Biomedical Engineering and Informatics - Tianjin, China 2009, 10, 1–4. [Google Scholar] [CrossRef]
56. Bashir S, et al. A Multicriteria Weighted Vote N/ABased Classifier Ensemble for Heart Disease Prediction. Computational Intelligence 2016, 32, 615. [Google Scholar] [CrossRef]
57. Banerjee, R.; Vempada, R.; Mandana, K.; Choudhury, A.; Dutta, P. Identifying coronary artery disease from photoplethysmogram. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct 1084–1088. 1084–1088. [CrossRef]
58. Paradkar, N.; Chowdhury, S. Coronary artery disease detection using photoplethysmography. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2017; 2017, 100–103. [Google Scholar] [CrossRef]