Chronic Disease Prediction Model Using Integration of DBSCAN, SMOTE-ENN, and Random Forest

Abstract

Heart disease (HD) is number one chronic disease and becomes a major cause of worldwide disability and death. Aside of HD, type 2 diabetes (T2D) is also as the most deathful diseases that causes serious issues if untreated and undetected. HD and T2D predictions are the most effective measures to control the HD and T2D. Thus, early HD and T2D predictions are important to help individuals in preventing the occurrence of the worst cases. This study proposes a chronic disease prediction model for HD and T2D prediction. The proposed study utilized random forest combined with DBSCAN as outlier detection method and SMOTE-ENN as data balancing method. Two HD datasets (Statlog and Cleveland) and one T2D dataset (NHIS Korea) were used for building the model and comparing the results with other existing machine learning (ML) algorithms, including GNB, LR, MLP, DT, and SVM. To measure the performance of the model, k-fold (10) cross-validation and several performance metrics including accuracy, precision, f-measure, and recall are applied in this study. The results show the model that we proposed outperforms other classification models, as well as previous studies, with accuracy rates 97.63%, 97.69%, and 94.85% for Statlog HD dataset, Cleveland HD dataset and NHIS T2D dataset, respectively. By utilizing the proposed model, it could increase the expectation in preventing the occurrence of the worst case and helping individuals in taking fast and precise actions when status of HD and T2D are detected. © 2022 IEEE.