Comparison of Machine Learning Models for Heart Disease Classification with Web-Based Implementation
Abstract
Heart disease has become one of the most concerning diseases in Indonesia according to research published in 2018 by the Health Ministry of Indonesia. Based on said research, 15 out of 1000 Indonesians suffer from heart disease. Furthermore, according to data published by the Health Ministry of Indonesia, 3 million premature deaths (under 60 years old) occurred in 2013 due to heart disease. Therefore, this research aims to develop a web-based system designed to aid health workers in screening for heart diseases and producing early diagnosis. In developing this system, 5 models were evaluated based on performance and the model with the best metrics was selected to be used in the final system. These models were: Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, and K-Nearest Neighbours. SMOTE and ADASYN was also used to deal with imbalanced data, and the resulting balanced data was used as additional training scenarios in order to compare the result with algorithms trained using imbalanced data. Cross validation, accuracy, precision, recall, f1-score, and ROC with AUC were set as evaluation metrics. Results show that Random Forest trained with data balanced using ADASYN achieved the highest AUC score of 0.920. Meanwhile, Logistic Regression scored lowest with an AUC score of 0.500. These results indicate that Random Forest is the most suitable for this system Therefore, Random Forest was selected as the algorithm to be used in the final system. Furthermore, this system has been tested successfully using the black-box method and is ready to be implemented.
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