Ensemble Learning for Heart Disease Diagnosis: A Voting Classifier Approach
Yogesh S1, Paneer Thanu Swaroop C2, Ruba Soundar K3
1Yogesh S, B.E, Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
2Paneer Thanu Swaroop C, B.E, Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
3Ruba Soundar K, Associate Professor (Sr. Grade), Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
Manuscript received on 07 August 2023 | Revised Manuscript received on 10 October 2023 | Manuscript Accepted on 15 November 2023 | Manuscript published on 30 November 2023 | PP: 1-11 | Volume-11 Issue-12, November 2023 | Retrieval Number: 100.1/ijese.J255509111023 | DOI: 10.35940/ijese.J2555.11111223
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cardiovascular disease remains a serious public health problem internationally, responsible for a considerable number of fatalities. Early and correct detection of cardiovascular illness is crucial for optimal care and control of the condition. In this paper, we present an ensemble learning technique that includes voting classifiers to increase the reliability of cardiovascular disease diagnosis. We obtained a set of data from five cardiology databases, including the Cleveland, Hungary, Switzerland, Long Beach VA, and Statlog (Heart) datasets, which provided us with a total of 1,189 entries. We employed a feature engineering approach to extract relevant features from the dataset, enabling us to acquire vital information to enhance our model’s performance. We trained and evaluated several machine learning algorithms, including Random Forests, MLP, K-Nearest Neighbours, Extra Trees, XGBoost, Support Vector Machines, AdaBoost, Decision Trees, Linear Discriminant Analysis, and Gradient Boosting. We then incorporated these models using voting classifiers to produce more reliable and accurate models. Our findings reveal that the proposed ensemble learning process outperforms standalone models and conventional ensemble approaches, obtaining an accuracy rate of 91.4%. Our technique is likely to benefit clinicians in the early diagnosis of heart problems and improve patient outcomes. This work has significant implications for the field of cardiology, suggesting that machine learning approaches may enhance both the reliability and accuracy of heart disease identification. The recommended ensemble learning technique can be applied in hospitals to improve patient care and ultimately mitigate the global impact of cardiovascular disease. Further study is needed to investigate the applications of predictive modelling in cardiology and other medical fields.
Keywords: Cardiovascular Disease; Heart Disease Diagnosis; Ensemble Learning; Voting Classifier; Machine Learning; Feature Engineering; Cleveland Dataset; Hungary Dataset; Switzerland Dataset; Long Beach VA Dataset; Statlog (Heart) Dataset
Scope of the Article: Machine Learning