ECTI TRANSACTIONS ON COMPUTER INFORMATION TECHNOLOGYVolume 14, No. 02, Month NOVEMBER, Year 2020, Pages 113 - 121
Diagnostic prediction models for cardiovascular disease risk using data mining techniques
Nongyao Nai-arun, Rungruttikarn Moungmai
Abstract Download PDFCardiovascular disease is the top national health problem that leads to a big number of deaths in Thailand. There is still a growing number of patients with the disease. Proactive measures of disease prevention and disease control are searching for risk groups. Therefore, people who are at risk can diagnose and manage themselves to reduce risk factors and adjust their behavior accordingly. For this reason, the idea of diagnostic prediction models for Cardiovascular was conducted. The data of patients from 126 health promoting hospitals and 12 hospitals in Saraburi Province were collected. Then, the analysis was done to establish 6 models namely logistic regression, random forest, back-propagation neural network, decision tree, naïve bayes and K-nearest neighbors. Moreover, 10-fold cross validation was applied into the process of each model. The results revealed that the logistic regression model achieved the highest accuracy rate, 99.940%, followed by the back-propagation neural network model, 98.506%. The best model should be developed as a web application to search for new patients or risk groups. It will help to prevent and control the disease quickly and also to reduce mortality.
Cardiovascular Disease, Logistic Regression, Random Forest, Back-propagation Neural Network, Nave-bayes, K-nearest Neighbors, Decision Tree