1. Introduction

2. Backgrounds

3. Illustrations

3.2. Medical Data Cleaning

The collected patient data provided by Chronic Disease Department of Guizhou CDC, data is based on 2010 natural population-based cohort baseline survey study, which take stratified cluster sampling (multi-stage cluster random sampling) method of 12 counties (city, area), 9280 residents which aged at 18, and conduct follow-up of grassroots medical institutions in 2016-2020, wherein cerebral stroke diagnosis of 395 people, median survival time after 6.50 years.

The nearly 400 cases of stroke cerebral patients baseline cross-sectional study survey data, which in the form of questionnaire survey contains more than 1000 columns information, including: basic situation (age, gender, residence), medical insurance, stroke morbidity situation, smoking, diet, drinking, exercise, stroke related diseases (blood pressure, blood sugar, coronary heart disease), and so on. Until the end of follow-up, all respondents signed informed consent by Guizhou Provincial Centers of Disease Control and Prevention approving (number: S2017~02).

Figure 2. Medical Data Cleaning.

Figure 2. Medical Data Cleaning.

According to the research needs of this project, we extracted 405 survey factors from this data as the correlation dependence analysis, and sorted statistical data about specific time and place of the onset stroke patients in 395 cases. At the same time, according to the above-mentioned data matched 395 non-stroke patients in the baseline survey cohort. By confirming the date and location of onset stroke patients, we associated with the corresponding meteorological elements forming data set by cleaning and merging, and imported the data set into the Gbase database on meterological cloud platform.

4. Data Fusion Based on Gbase Database

Figure 3. Column Family Storage.

Figure 3. Column Family Storage.

4.1. Data Fusion on Gbase

Sorted out the onset date and location of cerebral stroke population of Guizhou CDC, and processed the private data of the patients. Search the quality control multi-source gridded meteorological elements daily data according to the onset corresponding date and location of data set patients. Merge the two kinds of data to form a medical and meteorological fusion data set. According to the corresponding meaning of the existing data, combined with the database fields defined by the logical structure of the database table of “Tianqing” Big Data Cloud platform.

Figure 4. Load Data to Gbase..

Figure 4. Load Data to Gbase..

➢

loaddata gccli -ugbase -pgbase20110531

➢

load data infile ‘sftp://gbase:Cmadaas@2019@10.203.90.81/home/gbase/meteo-cvd-gz.txt.txt’ into table usr_gx.meteo_cvb_gz_tab data_format 3 fields terminated by ‘,’ null_value ‘NULL’ datetime format ‘%Y/%m/%d’ timestamp format ‘%Y/%m/%d’;

Figure 5. Dataset Fusion.

Figure 5. Dataset Fusion.

4.2. Dataset Fetch from “Tianqing” Big Data Platform

The integrated meteorological stroke data set is stored in the historical analysis database of “Tianqing” system, in order to facilitate the query and research analysis of project team members. We carry the corresponding data and publish the data set through the unified data environment interface on the meteorological big data cloud platform “Tianqing” (CMADaas).The meteorological big data cloud platform “Tianqing” (CMADaas) has designed a new storage technology, which upgrade function and expand the service version of the China Integrated Meteorological Information Service System(CIMISS) [8,9,10]. “Tianqing” platform is not only fully inherits CIMISS specifications, data types and interface service standards, but also significantly improves data quality, data storage time series, data processing efficiency and other aspects [14]. Meteorological data service interface provide the national unified, standard and rich data access service and application programming interface (API) for meteorological business and scientific research on “Tianqing” plarform (CMADaas), which provides the only authority data access service for application systems at national, provincial [15], local and county levels, and provides the registration and release of mass innovation interfaces. The management of the open data service interface. Project researchers can view the “meteorological stroke” fusion data set stored in the “Tianqing” history analysis database through the interface of the meteorological big data platform [16].

Figure 6. MUSIC Interface Read Fusion Dataset.

Figure 6. MUSIC Interface Read Fusion Dataset.

5. Data Processing and Correlation Analysis

5.2. Visualization of Data

In our study, considering the research kernel is association between meteorological factors and stroke onset, therefore, after completing the pretreatment of the data and aim to exhibit the analysis results more clearly and intuitively through visual analysis of the data [29]. It is conductive to better understand the distribution, trends, outliers and other information of the data, and then to estimate the potential relationship of the data [30]. According to the labeling logic of this study, in this paper, we performed a statistical work of the data incidence rate range, and the number of days included in each incidence rate, as shown in Figure 7. As shown from the figure, the distribution of the incidence rate basically conforms to the normal distribution.

Figure 7. The distribution of days included in the incidence rate.

Figure 7. The distribution of days included in the incidence rate.

For the research of association between meteorological factors and stroke incidence rate, we performed a statistical fusion data of 2010-2020 about stroke incidence ratee and meteorological factors respectively, for instance, concerning the mean (Mean), maximum (Max), minimum (Min), median (Median), standard deviation (Std). In this paper, the fields in the table contents are as follows: “Unit” represents unit, “Label” represents stroke incidence rate, “aver_rh” represents the daily average relative humidity, “aver_pres” represents the daily average air pressure, “aver_temp” represents the daily average temperature, “high_pres” represents the daily maximum air pressure, “high_temp” represents the daily maximum temperature, “low_pres” represents the daily minimum air pressure, “low_temp” represents the daily minimum temperature, “min_ rh” represents the daily minimum relative humidity, “diff_temp” represents the daily maximum temperature difference, “diff _pres” represents the daily maximum air pressure difference, as shown in Table 1.

5.4. Feature Evaluation

Pearson Correlation Coefficient (PCCs) can be used to explore the association between the predictive value (stroke incidence) and the characteristic variables (meteorological factors). The Pearson’s coefficient is a widely used method to measure the degree of correlation between two variables, and to be able to simultaneously assess the correlation between multiple meteorological features and stroke onset. Figure 13 shows the Pearson coefficient thermal maps of the various meteorological characteristics and stroke incidence in this study. The Pearson correlation coefficient formula is shown as following (2):

$${\mathrm{P}}_{\mathrm{x},\mathrm{y}}=\frac{\mathrm{E}\left[\right(\mathrm{x}-{\mathsf{\mu }}_{\mathrm{x}}\left)\right(\mathrm{y}-{\mathsf{\mu }}_{\mathrm{y}}\left)\right]}{{\mathsf{\sigma }}_{\mathrm{x}}{\mathsf{\sigma }}_{\mathrm{y}}},$$

(2)

The Pearson correlation coefficient is calculated as the quotient of covariance and standard deviation between two features, x and y are different features, $\mathsf{\mu }$ represents the expectation and $\mathsf{\sigma }$ represents the standard deviation.

To visually present the correlation between multiple variables, Figure 13 shows the Pearson coefficient thermodynamic map of individual meteorological features as well as stroke incidence (label) in this study. Each cell in the matrix graph represents the correlation between its corresponding horizontal and vertical two feature variables, and the darker the color, the stronger the correlation. By observing this thermodynamic map, it can be found that the Pearson coefficient of stroke incidence (label) and minimum relative humidity(min RH), maximum temperature(max TEMP), minimum air pressure(min pres) and other characteristics can be regarded as meteorological factors significantly associated with the incidence of stroke, and they show weak correlation themselves.

Combined with the above analysis, we see that stroke incidence increasing when meteorological factors at low humidity (less than 40% (RH<40%)), high temperature (temperature higher than 34℃), high humidity (relative humidity greater than 80% (RH >80%), temperature higher than 31℃); the 24-hour daily temperature difference varies between 6 and 10℃ (6 <△Tmax24h<10) and 60-80% at 60% (60%< RH <80%).

Figure 13. The Pearson correlation coefficients among various meteorological feature variables and correlations with cerebral stroke incidence.

Figure 13. The Pearson correlation coefficients among various meteorological feature variables and correlations with cerebral stroke incidence.

6. Conclusion and Discussion

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