Analysis of Machine Learning Algorithm for Prediction of Heart Disease

M M VISHAL, LAKHAN S KESARAGOPP, KUNDAN KULKARNI, MANIGANDAN S, PARTHASARATHY G

Abstract


In today’s era Heart disease has become one of the vital causes of death in the world.  The age group which has the major death rate because of heart disease is from  30-69. Therefore, the prediction of heart disease at the early stage is a prime challenge nowadays, because of many risk factors. Predicting and Analyzing heart disease using a single data mining approach does not provide us the best accuracy with precision. So in this project, we are using various Machine Learning Algorithm and Data Mining techniques like Random Forest, Naive Bayes algorithm, Decision Tree, Support Vector Machine in order to get the best accuracy with precision and to reduce the number of tests required to be carried out to find the result, and provide the results quickly. This project includes collecting the best attributes to analyze and predict the heart disease, and use various machine learning algorithms to successfully generate the accurate result. And ensures that heart disease is predicted at the early stage.


Keywords


Machine Learning, Data Mining, Random Forest, Decision Tree, Naive Bayes, Support Vector Machine.

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References


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DOI: https://doi.org/10.26483/ijarcs.v11i3.6532

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