Machine learning techniques for early heart failure prediction / Nur Shahellin Mansur Huang, Zaidah Ibrahim and Norizan Mat Diah

Mansur Huang, Nur Shahellin and Ibrahim, Zaidah and Mat Diah, Norizan (2021) Machine learning techniques for early heart failure prediction / Nur Shahellin Mansur Huang, Zaidah Ibrahim and Norizan Mat Diah. Malaysian Journal of Computing (MJoC), 6 (2): 6. pp. 872-884. ISSN 2600-8238

Abstract

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.

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
Mansur Huang, Nur Shahellin
shahellinnur@yahoo.com
Ibrahim, Zaidah
zaidah@fskm.uitm.edu.my
Mat Diah, Norizan
norizan@fskm.uitm.edu.my
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Neural networks (Computer science)
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences
Journal or Publication Title: Malaysian Journal of Computing (MJoC)
UiTM Journal Collections: UiTM Journal > Malaysian Journal of Computing (MJoC)
ISSN: 2600-8238
Volume: 6
Number: 2
Page Range: pp. 872-884
Keywords: Heart failure prediction, logistic regression, naive bayes, random forest, support vector machine
Date: October 2021
URI: https://ir.uitm.edu.my/id/eprint/61469
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