MACHINE LEARNING TECHNIQUES FOR EARLY HEART FAILURE PREDICTION
Nur Shahellin Mansur Huang1, Zaidah Ibrahim2 and Norizan Mat Diah3*
1,2,3 Faculty of Computer and Mathematical Sciences,
Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia
This paper discusses the performance of four popular machine learning techniques for predicting heart failure using a publicly available dataset from kaggle.com, which are Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and Logistic Regression (LR). They were selected due to their good performance in medical-related applications. Heart failure is a common public health problem, and there is a need to improve the management of heart failure cases to increase the survival rate. The vast amount of medical data related to heart failure and the availability of powerful computing devices allow researchers to conduct more experiments. The performance of the machine learning techniques was measured by accuracy, precision, recall, f1-score, sensitivity, and specificity in predicting heart failure with 13 symptoms or features. Experimental analysis showed that RF produces the highest performance score, which is 0.88 compared to SVM, NB, and LR. Further experiments with RF were also conducted to determine the important features in predicting heart failure, and the results indicated that all 13 symptoms or features are important.
Keywords: Heart Failure Prediction, Logistic Regression, Naive Bayes, Random Forest, Support Vector Machine.
Published On: 10 August 2021