Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Mar 15, 2024)
Date Submitted: May 15, 2023
Open Peer Review Period: May 15, 2023 - Jul 10, 2023
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Development of a risk assessment model for multimorbidity of diabetes, hypertension, and coronary heart disease with XGBoost in Primary Care in Shanghai, China: Retrospective Study
ABSTRACT
Background:
Multimorbidity has become an escalating health issue worldwide. Among various heterogeneous patterns of common multimorbidity, the cardiometabolic cluster is one of the most prevalent ones. Evaluating the risk of multimorbidity of the cardiometabolic pattern from the perspective of general practice has become a crucial agenda in primary care.
Objective:
The aim of this study was to develop a comprehensive risk assessment model for multimorbidities of diabetes, hypertension, and coronary heart disease of the elderly in the community based on big data in Shanghai, China.
Methods:
Retrospective data of 40,261 residents from 47 community health centers from 2017 to 2019 were extracted, including the residents' health records and health examination data, the hospital management information system data (HIS), and the imaging examination database. The machine learning algorithm, XGBoost was utilized for the construction of the comprehensive risk assessment model for multimorbidities of diabetes, hypertension, and coronary heart disease. Area under the receiver operating characteristic curve, accuracy, precision, recall, F1 Score and other indicators were used for model evaluation.
Results:
A total of 46 features was incorporated into the final comprehensive risk assessment model for multimorbidities of diabetes, hypertension, and coronary heart disease. The micro-average AUC value of the optimal model was 0.822. The macro average AUC value was 0.795. The weighted average AUC value was 0.784. These parameters showed a high superiority of the constructed model.
Conclusions:
The comprehensive risk assessment model for multimorbidities of diabetes, hypertension, and coronary heart disease based on XGBoost integrated medical and public health data of residents in community and uncovered multidimensional risk factors from four dimensions. It is of high directive value to implement the comprehensive risk assessment in improving the corresponding health management strategies for residents with multimorbidities.
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