Identification of the Most Prominent Feature Responsible for Accurate Heart Disease Prediction using Classification Algorithms

  • T Senthil Kumar, Sai Sameer Vennam, Sai Saketh Chamarthi

Abstract

Machine Learning algorithms have found their applications in various domains over the past decade. A class of machine learning algorithms that have garnered profound attention and are widely being used for data analysis are the classification algorithms. Classification algorithms are used to classify large scale data into two or more classes. There are a number of algorithms such as Support Vector Machine, KNN, Decision Tree, Random Forest which are used to perform classification. Heart disease is a major public health problem that has caused premature deaths all over the world. In the present decade, there is a need for a system to tackle this problem by using the latest technical developments. Many machine learning techniques have been employed independently to be able to predict the presence of heart diseases in individuals based on structured and unstructured healthcare data. Studies, where Principal Component Analysis(PCA) was used, have suggested that the accuracy rate is interlinked with the selection of the features from the available dataset. Thus, better feature selection increases the quality of the prediction model. In this study, we take reference from the study of Cleveland heart disease dataset from the UCI-Repository will be used to get the best combination of six features after testing each possible combination from all the thirteen features available against all the four classifiers mentioned above. Furthermore, this work will find the most prominent feature out of all the thirteen features by counting the number of occurrences of each element in the top hundred combinations giving the maximum score.

Published
2020-04-03
How to Cite
T Senthil Kumar, Sai Sameer Vennam, Sai Saketh Chamarthi. (2020). Identification of the Most Prominent Feature Responsible for Accurate Heart Disease Prediction using Classification Algorithms. International Journal of Advanced Science and Technology, 29(3), 7101 - 7110. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/7572
Section
Articles