Version 1
: Received: 25 October 2024 / Approved: 25 October 2024 / Online: 28 October 2024 (10:49:11 CET)
How to cite:
Lopukhova, E. A.; Yusupov, E. S.; Ibragimova, R. R.; Idrisova, G. M.; Mukhamadeev, T. R.; Grakhova, E. P.; Kutluyarov, R. V. Hybrid Integent Staging of Age-Related Macular Degeneration for Decision-Making on Patient Management Tactics. Preprints2024, 2024102033. https://doi.org/10.20944/preprints202410.2033.v1
Lopukhova, E. A.; Yusupov, E. S.; Ibragimova, R. R.; Idrisova, G. M.; Mukhamadeev, T. R.; Grakhova, E. P.; Kutluyarov, R. V. Hybrid Integent Staging of Age-Related Macular Degeneration for Decision-Making on Patient Management Tactics. Preprints 2024, 2024102033. https://doi.org/10.20944/preprints202410.2033.v1
Lopukhova, E. A.; Yusupov, E. S.; Ibragimova, R. R.; Idrisova, G. M.; Mukhamadeev, T. R.; Grakhova, E. P.; Kutluyarov, R. V. Hybrid Integent Staging of Age-Related Macular Degeneration for Decision-Making on Patient Management Tactics. Preprints2024, 2024102033. https://doi.org/10.20944/preprints202410.2033.v1
APA Style
Lopukhova, E. A., Yusupov, E. S., Ibragimova, R. R., Idrisova, G. M., Mukhamadeev, T. R., Grakhova, E. P., & Kutluyarov, R. V. (2024). Hybrid Integent Staging of Age-Related Macular Degeneration for Decision-Making on Patient Management Tactics. Preprints. https://doi.org/10.20944/preprints202410.2033.v1
Chicago/Turabian Style
Lopukhova, E. A., Elizaveta P. Grakhova and Ruslan V. Kutluyarov. 2024 "Hybrid Integent Staging of Age-Related Macular Degeneration for Decision-Making on Patient Management Tactics" Preprints. https://doi.org/10.20944/preprints202410.2033.v1
Abstract
Treatment efficacy for age-related macular degeneration relies on early diagnosis and precise determination of the disease stage. It involves analyzing biomarkers in retinal images, which can be challenging when handling a large flow of patients and compromise the quality of healthcare services. Clinical decision support systems offer a solution to this issue by employing intelligent algorithms to recognize biomarkers and specify the age-related macular degeneration stage through the analysis of retinal images. However, different stages of age-related macular degeneration may exhibit similar biomarkers, complicating the application of intelligent algorithms. This paper introduces an approach to overcome these challenges using hybrid and hierarchical classification. By leveraging the hybrid structure of the classifier, we can effectively manage issues commonly encountered with medical data sets, such as class imbalance and strong correlations between variables. The modifications to the intelligent algorithm proposed in this work for staging age-related macular degeneration resulted in an increase in average accuracy, sensitivity, and specificity by 20% compared to initial values. The Cohen’s Kappa coefficient used for consistency estimation between the regression model and expert assessments of the intermediate class severity was 0.708, indicating a high level of agreement.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
