Heart Disease Diagnosis: A Machine Learning Approach

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Author(s): Siddhartha Kumar Arjaria (Rajkiya Engineering College Banda, India)and Abhishek Singh Rathore (Independent Researcher, India)
Copyright: 2019
Pages: 21
Source title: Advanced Classification Techniques for Healthcare Analysis
Source Author(s)/Editor(s): Chinmay Chakraborty (Birla Institute of Technology Mesra, India)
DOI: 10.4018/978-1-5225-7796-6.ch008

Keywords: Data Analysis & Statistics / Library & Information Science / Medical Information Science Reference / Medical Technologies

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Abstract

In the modern era of information technology, machine learning algorithms are used in different domains for boosting the quality of decision making. The correct decision making about the disease diagnosis is one of the applications where these approaches are applied successfully for assisting the doctors. Correct and timely diagnosis of disease is the primary requirement of effective treatment. Today, one of the most leading causes of death is heart disease. This chapter deals with the application of different machine learning algorithms for effective heart disease diagnosis. Diagnosis through the machine learning algorithms involves the three major steps, data preprocessing, feature selection, and classification. The chapter covers the experimental study of performance of SVM, ANN, logistic regression, random forest, KNN, AdaBoost, Naive Bayes, decision tree, SGD, CN2 rule inducer approaches.

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Heart Disease Diagnosis: A Machine Learning Approach

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