Efficient Heart Disease Prediction Using Ensemble Classifiers in Hybrid Dataset

Authors

  • P.Sudha Research Scholar, PG & Research Department of Computer Science Govt. Arts College for Men, Nandanam(Autonomous) Chennai
  • R. Thirumalai Selvi Associate Professor, PG & Research Department of Computer Science Govt. Arts College for Men, Nandanam(Autonomous) Chennai

Keywords:

learning, support vector machine, k-nearest neighbor, logistic regression, confusion matrix, google colab notebook

Abstract

The heart is a vital organ in the human body. Heart diseases can lead to death in humans. So it is important to detect and predict its occurrence at an early stage to avoid fatal deaths. Some of the main cardiovascular diseases include coronary heart disease, stroke, heart attack, valvular heart disease (VHD), etc. So with the help of machine learning, which is a branch of artificial intelligence, we can detect and predict the occurrence of heart diseases. The proposed study uses two categories of data sets taken from the UCI machine learning repository, both for training and testing purposes. Various features present in the data set are taken into consideration. Among these features, a few affect the valve, leading to malfunctioning of the heart. Machine learning algorithms such as Support Vector Machine (SVM), Random Forest Classifier, K-nearest neighbor, logistic regression, and ensemble algorithms are used to predict the classification metrics. Algorithms are executed stand-alone as well as combined. This study shows a variation in accuracy. The novel idea implemented here is to combine two data sets and predict the accuracy of the presence of heart disease or not. Experimental results showed KNN having a high accuracy of 95%, and the ensemble model (combining linear SVM, radial SVM, and, logistic regression) shows a high accuracy of 99% in the hybrid data set. For implementation, the tool used is Python Programming (Google Co laboratory) Notebook.

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Published

2023-10-25

How to Cite

P.Sudha, & R. Thirumalai Selvi. (2023). Efficient Heart Disease Prediction Using Ensemble Classifiers in Hybrid Dataset. Chinese Journal of Computational Mechanics, (5), 512–523. Retrieved from http://jslxxb.cn/index.php/jslxxb/article/view/4396

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Section

Articles