A Study on Data-Driven Personalized Disease Prediction Models Using Artificial Intelligence

Hwa Min Jeong; In Cheol Park; Jae Sung Choi

doi:10.5762/KAIS.2024.25.12.28

A Study on Data-Driven Personalized Disease Prediction Models Using Artificial Intelligence

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인공지능을 활용한 데이터 기반 개인 맞춤형 질병 예측 모델 연구

Type: Academic journal

Author: Hwa Min Jeong (타우데이타) In Cheol Park (선문대학교) Jae Sung Choi (선문대학교)

Journal: Korea Academy Industrial Cooperation Society Journal of the Korea Academia-Industrial cooperation Society Vol.25 No.12 KCI Accredited Journals

Published: 2024.12

Pages: 28 - 35 (8page)

DOI: 10.5762/KAIS.2024.25.12.28

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A Study on Data-Driven Personalized Disease Prediction Models Using Artificial Intelligence

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This study investigates the advancements in data-driven personal health management systems, and explores the application of artificial intelligence (AI) to build customized disease prediction models. A personal health management system integrates individual medical records and lifestyle data, enabling real-time health monitoring, and providing tailored disease prediction services. In particular, this research employs Korean health examination data to develop and evaluate diabetes prediction models by using logistic regression, the decision tree, gradient boosting, and XGBoost. Results demonstrate that gradient boosting and XGBoost models achieved superior predictive performance, successfully enhancing accuracy through real-time data feedback and hyperparameter optimization. This study highlights the need for data-driven personalized health management, and demonstrates the critical role of AI technology in healthcare. Additionally, it underscores the commercial potential of data-driven health management systems, which are expected to grow at a 47.99% CAGR, reaching approximately US$7.7 billion by 2026 in the global marketplace. These findings suggest significant opportunities for the proposed technology in both domestic and international markets

#Data-driven Health Management #Artificial Intelligence #Diabetes Prediction #Machine Learning #Predictive Analytics

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J. H. Ha, G. B. Gong, D. K. Park, “Development of a deep learning model for predicting Alzheimer’s disease using multi-omics data”, Journal of the Korea Institute of Information Technology, Vol.20, No.7, pp.29-37, 2022. DOI: https://doi.org/10.14801/jkiit.2022.20.7.29

B. J. Lee, “Hypertension prediction model using machine learning based on socio-demographic characteristics”, Journal of Information Processing Society: Software and Data Engineering, Vol.10, No.11, pp.541-546, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.11.541

T. H. Lim, G. O. Lim, S. W. Jung, S. C. Han, “Disease diagnosis using deep learning based on military medical data”, Journal of Digital Contents Society, Vol.22, No.9, pp.1359-1367, 2021. DOI: https://doi.org/10.9728/dcs.2021.22.9.1359

S. H. Byun, “Healthcare application utilization plan based on disease prediction algorithms – focused on prediction of five national cancers and 12 major diseases”, International e-Business Society, Vol.24, No.5, pp.119-137, 2023. DOI: https://doi.org/10.20462/tebs.2023.10.24.5.119

Korea Health Industry Development Institute, Personal Health Record (PHR), 2023. https://www.khidi.or.kr/board

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