Study on prediction and diagnosis AI model of frequent chronic diseases based on health checkup big data
Abstract
The purpose of this study is to develop a disease prediction model that can evaluate diagnostic test results based on a machine learning model and big data analysis algorithms for automated judgment of health chuck-up results. The research method used the catboost algorithm for data pretreatment and analysis. The original data was divided into learning data and test data to ensure 21,140 effective data consisting of 27 properties and to develop and utilize predictive models. Learning data was used as input data for the development of predictive models, and the test data was divided into data for the performance evaluation of the predictive model. Random forest analysis algorithms were used to analyze testing and determination accuracy that affect disease determination, and forecasting model performance analysis was analyzed by accuracy, ROC (ROC) Area, Confusion Matrix, Precision, and Recall indicators. As a result of random forest analysis, both diabetes and two -ventilation diseases were analyzed to be used as a commercial platform model by analyzing more than 90% forecast accuracy. The results of this study found that using big data analysis and machine learning, it is possible to determine and predict specific diseases based on health check-up data.
Keywords
Full Text:
PDFReferences
1. Kim HS. A Study on the Efficient Policy of Health Examination based on Comparing Private Health Sector with Public Health Sector [PhD thesis]. Kyunghee University; 2010.
2. Min WG. New Criteria for the Quantitative Evaluation of Quality Control Results [PhD thesis]. Seoul National University; 1992.
3. Kim YT, Chae BS, Hwang BJ. The effect of physical environments in the comprehensive health examination center on medical service value, satisfaction and switching barrier (Korean). Journal of Service Research and Studies 2019; 9(4): 63–80. doi: 10.18807/jsrs.2019.9.4.063
4. Bartels PH, Thompson D, Wedber JE. Diagnostic and prognositc decision support systems. Pathologica 1995; 87(3): 221–236.
5. Swets JA. Measuring the accuracy of diagnostic systems. Science 1988; 240(4857): 1285–1293. doi: 10.1126/science.3287615
6. Singh JA, Strand V. Gout is associated with more comorbidities, poorer health-related quality of life and higher healthcare utilisation in US veterans. Annals of the Rheumatic Diseases 2008; 67(9): 1310–1316. doi: 10.1136/ard.2007.081604
7. Lee SJ, Hirsch JD, Terkeltaub R, et al. Perceptions of disease and health-related quality of life among patients with gout. Rheumatology 2009; 48(5): 582–586. doi: 10.1093/rheumatology/kep047
8. Choi HK, Ford ES, Li C, Curhan G. Prevalence of the metabolic syndrome in patients with gout: The third national health and nutrition examination survey. Arthritis & Rheumatism 2007; 57(1): 109–115. doi: 10.1002/art.22466
9. Oh JW, Kang JK. Exploration of disease occurrence prediction model based on clinical pathology results using machine learning. In: Tripathy HK, Mishra S, Mallick PK, et al. (editors). Technical Advancements of Machine Learning in Healthcare. Springer, Singapore; 2021. pp. 377–388.
10. Krizhevsky A, Sutskever I, Hilton GE. ImageNet classification with deep convolutional neural networks. Communications of the ACM 2017; 60(6): 84–90. doi: 10.1145/3065386
11. Murphy KP. Machine Learning: A Probabilistic Perspective. The MIT Press; 2015.
12. Buyya R, Calheiros RN, Dastjerdi AV. Big Data: Principles and Paradigms, 1st ed. Morgan Kaufmann; 2016.
13. Géron A. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd ed. O’Reilly Media; 2019.
14. Kim SR. The current situation and the suggestions of AI & Law research in legal reasoning (Korean). IT and Law Review 2011; 5: 319–346.
15. Priyopradono B, Manongga D, Utomo WH. Spatial social network analysis: Program Pengembangan Usaha Agribisnis Perdesaan (PUAP) or an exertion development program in supporting the region revitalization development. Social Networking 2013; 2(2): 63–76. doi: 10.4236/sn.2013.22008
16. Lu H, Li Y, Chen M, et al. Brain intelligence: Go beyond Artificial Intelligence. Mobile Networks and Applications 2017; 23(2): 368–375. doi: 10.1007/s11036-017-0932-8
17. Press G. Top 10 hot Artificial Intelligence (AI) technologies. Available online: https://www.forbes.com/sites/gilpress/2017/01/23/top-10-hot-artificial-intelligence-ai-technologies/?sh=68ea0f121928 (accessed on 3 August 2023).
18. Lee HT, Lee SW, Cho JW, Cho IS. Analysis of feature importance of ship’s berthing velocity using classification algorithms of machine learning. Journal of the Korean Society of Marine Environment & Safety 2020; 26(2): 139–148. doi: 10.7837/kosomes.2020.26.2.139
19. Waterman DA. A Guide to Expert Systems. Addison-Wesley Publishing Company; 1986.
20. Cho IS. Assessing the quality of structured data entry for the secondary use of electronic medical records. Journal of Korean Society of Medical Informatics 2009; 15(4): 423–431. doi: 10.4258/jksmi.2009.15.4.423
21. World Health Organization. Improving Data Quality: A Guide for Developing Countries. WHO Regional Office for the Western Pacific; 2003.
22. Malik A, Schumacher HR, Dinnella JE, Clayburne GM. Clinical diagnostic criteria for gout: Comparison with the gold standard of synovial fluid crystal analysis. Journal of Clinical Rheumatology 2009; 15(1): 22–24. doi: 10.1097/RHU.0b013e3181945b79
23. Neogi T, Jansen TL, Dalbeth N, et al. 2015 gout classification criteria: An American college of rheumatology/European league against rheumatism collaborative initiative. Arthritis & Rheumatology 2015; 67(10): 2557–2568. doi: 10.1136/annrheumdis-2015-208237
DOI: https://doi.org/10.32629/jai.v7i3.999
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Jae Young Park, Jai-Woo Oh
License URL: https://creativecommons.org/licenses/by-nc/4.0/