Machine learning-based prediction models for morbidity and mortality risk of cardiometabolic diseases

• Project/Area Number: 24K13482
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 58030:Hygiene and public health-related: excluding laboratory approach
• Research Institution: Fukushima Medical University
• Principal Investigator: 馬 恩博 福島県立医科大学, 公私立大学の部局等, 准教授 (00590770)
• Co-Investigator(Kenkyū-buntansha): 大平 哲也 福島県立医科大学, 医学部, 教授 (50448031)
• Project Period (FY): 2024-04-01 – 2027-03-31
• Project Status: Granted (Fiscal Year 2024)
• Budget Amount: ¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000); Fiscal Year 2026: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000); Fiscal Year 2025: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000); Fiscal Year 2024: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
• Keywords: machine learning / cardiovascular disease / diabetes mellitus / prediction model / life expectancy

Outline of Research at the Start

Using conventional statistical analysis methods may not be sufficient to elucidate complex causation, comorbidities, or treatment of cardiometabolic diseases. Based on the specific health checkup data, we will ensemble machine learning algorithms to build prediction models of cardiometabolic disease risk and estimate attributable healthy life expectancies with care levels. This study will provide novel information on preventing and treating cardiometabolic diseases in Japanese by handling clinicopathologic features that exhibit a comprehensive nonlinear and interaction association.