Cardiovascular Disease Prediction using Machine Learning Algorithms

The world which we see today is getting advanced each year, this is due to some of the most promising factors such as development in the field of science and technology, industrialization, globalization, and various other aspects. Considering the remarkable development in surroundings we can also see a drastic change in the human’s health. Humans are facing a lot of health issues because of the modern food and the lifestyle they have adopted. Junk food and sedimentary working culture are the major causes of many diseases. Heartrelated diseases are one of the hazardous diseases the world is facing today. 17 million deaths worldwide are due to Cardiovascular Disease (CVD) which contributes to 31%. With the increase in heart diseases, there are several pieces of equipment and sophisticated machines to predict the heart condition. This paper mainly concentrates on a system where a patient can easily check the condition of their heart. The proposed system considers some of the famous algorithms of Machine Learning which includes Naive Bayes, Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest. Cleveland dataset from UCI repository with 14 attributes and 303 instances is used. The patient just needs to provide some of the information as input and the system will perform predictions on the condition of the heart and other factors.