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Volume & Issue no: Volume 11, Issue 4, April 2022

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Title:
Heart Disease Detection Using Feature Selection Algorithms in Machine Learning
Author Name:
Neetu Kumari, Dr. Anita Ganpati
Abstract:
ABSTRACT Heart disease is a prevalent concern around the world. There is a rapid growth in the number of patients becoming victims of heart disease and many of them die of heart disease if not detected in time. A heart attack occurs instantly when a coronary artery becomes blocked completely. Therefore, it is important to quickly detect the heart disease in the early stages to save lives of people. In this paper six different feature selection algorithms and subsequently six machine learning classifiers were used. XGBoost, Multi-Layer Perceptron, k- Nearest Neighbor, Random Forest Classifier, Support Vector Machine Classifier (SVM), and Stochastic Gradient Descent (SGD) were used to predict the heart disease. The algorithms were evaluated with and without feature selection. The experimentation was done by using Python programming language. The highest accuracy was obtained by using XGBoost ensemble algorithm with feature selection algorithms. Keywords: Heart Disease, Machine Learning, XGBoost, Multi Layer Perceptron (MLP)
Cite this article:
Neetu Kumari, Dr. Anita Ganpati , " Heart Disease Detection Using Feature Selection Algorithms in Machine Learning" , International Journal of Application or Innovation in Engineering & Management (IJAIEM) , Volume 11, Issue 4, April 2022 , pp. 019-030 , ISSN 2319 - 4847.
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