Classification Of Heart Disease Using Feature Selection and Machine Learning Techniques

Sondos Jameel Mukhyber

doi:10.47134/pslse.v2i3.386

Authors

Sondos Jameel Mukhyber General Directorate for Education of Diyala, Iraq

DOI:

https://doi.org/10.47134/pslse.v2i3.386

Keywords:

Heart Disease, Support Vector Machine, Logistic Regression, Decision Trees, Artificial Neural Network

Abstract

Heart disease is a complex disease that affects a large number of people worldwide. The timely and accurate detection of heart disease is critical in healthcare, particularly in the field of cardiology. In various fields around the world, machine learning is used. There are no exceptions in the healthcare sector. Machine learning can be crucial in determining whether or not there will be locomotor abnormalities, heart ailments, and other conditions. If foreseen far in advance, such information can offer crucial intuitions to doctors, who can then modify their diagnosis and approach per patient. in this paper it has been used a variety of machine learning techniques and used the heart disease dataset to evaluate its performance using different metrics for evaluation, such as accuracy, precision, recall ,and F-measure. For this purpose, it has been used five classifiers of machine learning such as Support Vector Machine, Gaussian Naïve Bayes, Decision Trees, Artificial Neural Network, and Logistic Regression. Furthermore, it has been check their accuracy on the standard heart disease dataset by performing certain pre-processing of dataset, and feature section. Finally, the experimental result indicated that the accuracy of the prediction classifiers.

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