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.