AI-based prediction for the risk of coronary heart disease among patients with type 2 diabetes mellitus.

Journal: Scientific reports
Published Date:

Abstract

Type 2 diabetes mellitus (T2DM) is one common chronic disease caused by insulin secretion disorder that often leads to severe outcomes and even death due to complications, among which coronary heart disease (CHD) represents the most common and severe one. Given a huge number of T2DM patients, it is thus increasingly important to identify the ones with high risks of CHD complication but the quantitative method is still not available. Here, we first curated a dataset of 1,273 T2DM patients including 304 and 969 ones with or without CHD, respectively. We then trained an artificial intelligence (AI) model using randomly selected 4/5 of the dataset and use the rest data to validate the performance of the model. The result showed that the model achieved an AUC of 0.77 (fivefold cross-validation) on the training dataset and 0.80 on the testing dataset. To further confirm the performance of the presented model, we recruited 1,253 new T2DM patients as totally independent testing dataset including 200 and 1,053 ones with or without CHD. And the model achieved an AUC of 0.71. In addition, we implemented a model to quantitatively evaluate the risk contribution of each feature, which is thus able to present personalized guidance for specific individuals. Finally, an online web server for the model was built. This study presented an AI model to determine the risk of T2DM patients to develop to CHD, which has potential value in providing early warning personalized guidance of CHD risk for both T2DM patients and clinicians.

Authors

  • Rui Fan
    Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China.
  • Ning Zhang
    Institute of Nuclear Agricultural Sciences, Zhejiang University, Hangzhou, 310058, China.
  • Longyan Yang
    Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Jing Ke
    Beijing Key Laboratory of Diabetes Research and Care, Center for Endocrine Metabolism and Immune Diseases, Lu He Hospital Capital Medical University, Beijing, 101149, China.
  • Dong Zhao
    Collaborative Innovation Center of Judicial Civilization, Key Laboratory of Evidence Science, Ministry of Education, China University of Political Science and Law, Beijing 100088, China.
  • Qinghua Cui
    Department of Biomedical Informatics, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University, Beijing, China.