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

• Published in: Scientific reports Vol. 10; no. 1; p. 14457
• Main Authors: Fan, Rui, Zhang, Ning, Yang, Longyan, Ke, Jing, Zhao, Dong, Cui, Qinghua
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02-09-2020; Nature Publishing Group
• Subjects: 692/308; 692/4019; 692/499; 692/699; 692/700/139; Aged; Artificial Intelligence; Cardiovascular diseases; Chloroplasts; Coronary Disease - diagnosis; Coronary Disease - epidemiology; Coronary Disease - etiology; Coronary Disease - physiopathology; Deoxyribonucleic acid; Diabetes Mellitus, Type 2 - complications; Diabetes Mellitus, Type 2 - diagnosis; Diabetes Mellitus, Type 2 - epidemiology; Diabetes Mellitus, Type 2 - physiopathology; Dietary supplements; Divergence; DNA; Echinacea; Female; Genomes; Humanities and Social Sciences; Humans; Immune response; Insulin secretion; Male; Middle Aged; multidisciplinary; Nucleotide sequence; Respiratory tract diseases; Risk Factors; Science; Science (multidisciplinary); Sibling species; Single-nucleotide polymorphism; Species
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-71321-2

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.