Abstract:
Accurate prediction of cardiovascular disease is necessary and considered to be a difficult attempt to treat a patient effectively
before a heart attack occurs. According to recent studies, heart disease is said to be one of the leading origins of death
worldwide. Early identification of CHD can assist to reduce death rates. When it comes to prediction using traditional
methodologies, the difficulty arises in the intricacy of the data and relationships. This research is aimed at applying recent
machine learning technology to identify heart disease from past medical data to uncover correlations in data that can greatly
improve the accuracy of prediction rates using various machine learning models. Models have been implemented using naive
Bayes, random forest algorithms, and the combinations of two models such as naive Bayes and random forest methods. These
methods offer numerous attributes associated with heart disease. This proposed system foresees the chance of rising heart
disease. The proposed system uses 14 parameters such as age, sex, quick blood sugar, chest discomfort, and other medical
parameters which are used in the proposed system. Our proposed systems find the probability of developing heart disease in
percentages as well as the accuracy level (accuracy of 93%). Finally, this proposed method will support the doctors to analyze
the heart patients competently.
