Ensemble Based Machine Learning Model for Heart Disease Prediction

Ashley Bryan Ambrews, Ervin Gubin Moung, Ali Farzamnia, Farashazillah Yahya, Sigeru Omatu, Lorita Angeline

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The World Health Organization reports that more than 10 million people worldwide die each year due to heart disease. In this project, an ensemble-based machine learning (ML) model is proposed for the prediction of heart disease. The proposed method was evaluated on two datasets: UCI heart disease and Framingham heart study. Various ML such as K-Nearest Neighbors, Naïve Bayes, Decision Tree, Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF) were carried out to predict the outcome of heart disease. Additionally, the Stacking and Voting ensemble approaches are also employed for heart disease prediction. All ML classifiers evaluated on both datasets have their performance metrics analyzed and compared. The results of all ML classifiers assessed on the Framingham dataset is inferior than that of the UCI heart disease dataset. For individual ML classifier, SVM achieved the highest accuracy for UCI heart disease with 91.09%, whereas LR achieved the highest accuracy for the Framingham heart study with 85.38%. However, it should be noted that RF trained on the UCI dataset is the best ML for prioritizing Sensitivity (92.52%). Finally, with the exception of Sensitivity, the Voting approach is strongly suggested because it outperforms the other classifiers noticeably where it achieved Accuracy (91.96%), F1 (91.69%), Sensitivity (91.72%), Specificity (90.77%), and Precision (92.40%) on UCI dataset.

Original languageEnglish
Title of host publication2022 International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665491495, 9781665491488
ISBN (Print)9781665491501
DOIs
Publication statusPublished - 30 Dec 2022
Externally publishedYes
Event3rd International Conference on Communications, Information, Electronic and Energy Systems - Virtual, Online, Bulgaria
Duration: 24 Nov 202226 Nov 2022
Conference number: 3
https://ieeexplore.ieee.org/xpl/conhome/9989537/proceeding
https://ciees.eu/index.php/previous-ciees/ciees-proceedings/ciees2022

Conference

Conference3rd International Conference on Communications, Information, Electronic and Energy Systems
Abbreviated titleCIEES 2022
Country/TerritoryBulgaria
CityVirtual, Online
Period24/11/2226/11/22
Internet address

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