Early Exposing Cardiovascular Disease Identification Using Machine Learning Approach

Nowadays Cardiovascular disease is most important topics in medical sector. Numerous people all over the world affected with this disease and it is going alarming to the world population. The main goal of this study to predict the heart disease probability, find out the best algorithms for these types of data set and implement statistical analysis to find the relation among the attributes. While doing prediction analysis, we used some popular machine learning algorithms such as Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB) and Random Forest (RF). Decision Tree and Random Forest algorithms are gave the best accuracy rate among the algorithms and both are exactly same 96%. The statistical analysis of this study is clearly demonstrate that, population with higher heart rate, typical angina and non-anginal pain are most affected in disease. Hopefully, this study will be helpful for medical researchers and make decision for best machine learning algorithm.