Heart Disease Prediction Using Outlier Removal based Max Voting Ensemble Method

Authors

  • Pralhad Chapagain Kantipur Engineering College, Dhapakhel, Lalitpur Nepal

DOI:

https://doi.org/10.3126/injet.v1i1.60933

Keywords:

Heart Disease, Max Voting, Ensemble, Outlier Removal, XGBOOST, Decision Tree, KNN, SVM, Gradient Boost

Abstract

Heart disease has emerged as a serious health concern for many individuals due to its high death rate around the world. The routine clinical data analysis has a significant difficulty in the early diagnosis of cardiac disease. The identification of cardiac disease may benefit from the use of machine learning. To improve machine learning models, several studies have previously been conducted. The suggested study uses the maximum voting ensemble technique of classification to effectively identify heart disease. The suggested classifier is a more reliable and accurate approach. To identify and eliminate outliers, conduct Inter quartile range outlier removal and min-max normalization during preprocessing. Accuracy, Precision, Recall, and F1 Score are calculated and evaluated against various models. For the heart disease dataset collected from the Kaggle, the suggested max voting ensemble classifier has an accuracy of 99.22%.

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Published

2023-12-21

How to Cite

Chapagain, P. (2023). Heart Disease Prediction Using Outlier Removal based Max Voting Ensemble Method. International Journal on Engineering Technology, 1(1), 83–101. https://doi.org/10.3126/injet.v1i1.60933

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Section

Articles