Effective Heart Disease Prediction Using Machine Learning

Abstract

Cardiovascular disease is a leading cause of death in this period. The number of deaths among both males and females increases each day due to heart disease. For example, researchers used machine learning and data mining techniques to support the prognosis of heart disease in healthcare. However, using these techniques there may be a smart system of Heart Disease prediction that is quicker and more proficient than the usual system of diagnosis. The objective of this paper is to use data mining strategies and numerous machine learning algorithms such as Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, Random Forest, and Various Ensemble Method to gain a more accurate measure. We also suggested two of our own models in this paper. In this paper, a UCI repository Cleveland dataset is used. The sole aim of this paper is to use Data Mining and Machine Learning Techniques to find hidden patterns.