A Cardiovascular Disease Prediction using Machine Learning Algorithms

Abstract

Heart Diseases have shown a tremendous hit in this modern age. As doctors deal with precious human life, it is very important for them to be right their results. Thus, an application was developed which can predict the vulnerability of heart disease, given basic symptoms like   age, gender, pulse rate, resting blood pressure, cholesterol, fasting blood sugar, resting electrocardiographic results, exercise induced angina, ST depression ST segment the slope at peak exercise, number of major vessels colored by fluoroscopy and maximum heart rate achieved. This can be used by doctors to re heck and confirm on their patient’s condition. In the existing surveys they have considered only 10 features for prediction, but in this proposed research work 14 necessary features were taken into consideration. Also, this paper presents a comparative analysis of machine learning techniques like Random Forest (RF), Logistic Regression, Support Vector Machine (SVM), and Naïve Bayes in the classification of cardiovascular disease. By the comparative analysis, machine learning algorithm Random Forest has proven to be the most accurate and reliable algorithm and hence used in the proposed system. This system also provides the relation between diabetes and how much it influences heart disease