Abstract
Heart disease, one of the major causes of mortality worldwide, can be mitigated by early heart disease diagnosis. A clinical decision support system (CDSS) can be used to diagnose the subjects' heart disease status earlier. This study proposes an effective heart disease prediction model (HDPM) for a CDSS which consists of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and eliminate the outliers, a hybrid Synthetic Minority Over-sampling Technique-Edited Nearest Neighbor (SMOTE-ENN) to balance the training data distribution and XGBoost to predict heart disease. Two publicly available datasets (Statlog and Cleveland) were used to build the model and compare the results with those of other models (naive bayes (NB), logistic regression (LR), multilayer perceptron (MLP), support vector machine (SVM), decision tree (DT), and random forest (RF)) and of previous study results. The results revealed that the proposed model outperformed other models and previous study results by achieving accuracies of 95.90% and 98.40% for Statlog and Cleveland datasets, respectively. In addition, we designed and developed the prototype of the Heart Disease CDSS (HDCDSS) to help doctors/clinicians diagnose the patients'/subjects' heart disease status based on their current condition. Therefore, early treatment could be conducted to prevent the deaths caused by late heart disease diagnosis.
Highlights
Heart disease is a cardiovascular disease (CVD) that remains the number one cause of death globally and contributes to approximately 30% of all global deaths [1]
We selected six state-of-the-art machine learning algorithms (MLAs) (NB, logistic regression (LR), multilayer perceptron (MLP), support vector machine (SVM), DT, and random forest (RF)) that have been widely used in the research community and have a proven track record for accuracy and efficiency for comparison
The findings revealed that the proposed model outperformed other models by achieving acc, pre, rec/sec, f up to 95.90%, 97.14%, 94.67%, 95.35% for dataset I and 98.40%, 98.57%, 98.33%, 98.32% for dataset II, respectively
Summary
Heart disease is a cardiovascular disease (CVD) that remains the number one cause of death globally and contributes to approximately 30% of all global deaths [1]. The American Heart Association reported that nearly half of American adults are affected by CVDs, equating to nearly 121.5 million adults [2]. In Korea, heart disease is among the top three leading causes of death and contributed to nearly 45% of total deaths in 2018 [3]. Several risk factors that can lead to heart disease include unhealthy diet, physical inactivity, and excessive use of tobacco and alcohol. These risk factors can be minimized by practicing good daily lifestyle such as salt reduction in the
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