Construction and implementation of an AI prediction model for the onset of cardiovascular disease based on 700,000 people and 43 years of large-scale health checkup data

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K08034
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 53020:Cardiology-related
• Research Institution: Kagoshima University
• Principal Investigator: KAWASOE SHIN 鹿児島大学, 医歯学総合研究科, 特任講師 (00810201)
• Co-Investigator(Kenkyū-buntansha): 窪薗 琢郎 鹿児島大学, 医歯学域医学系, 講師 (00598013) ; 大石 充 鹿児島大学, 医歯学域医学系, 教授 (50335345)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: AI / データベース研究 / 健康診断 / 心血管疾患

Outline of Final Research Achievements

Visualization of health checkup data and preprocessing to AI application were performed, and LAMP and machine learning algorithms were applied to the data to create models. Hyperparameters were tuned and optimized for several machine learning models (random forest, XGBoosting, logistic regression, neural network, support vector machine, and others). Models were created for hypertension, chronic kidney disease, metabolic syndrome, and atherosclerosis (high baPWV) as outcomes. The results of the research were presented at several domestic and international conferences, and the results are being published in a series of papers.

Free Research Field

循環器内科学

Academic Significance and Societal Importance of the Research Achievements

超少子高齢化社会への突入と医療の高度化・高額化に伴い、わが国の医療財政は逼迫しており、疾患の予防および早期発見による医療費の抑制が急務である。我々は43年間にわたる70万人の健診データをもとにして、個人単位での疾患の早期発見・早期治療に役立つ心血管疾患発症予測の人工知能（AI）モデルを構築・実装した。さらにそのアルゴリズムを保健指導の場で実際に指導に役立てるべく、アプリ化を現在進めている。健康寿命の延伸と医療費の抑制に寄与する、世界に先駆けた新たな医療モデルであるものと考えている。