WEB BASED HEART DISEASE PREDICTION MODEL USING MACHINE LEARNING TECHNIQUE

The cases of heart diseases are increasing at a rapid rate and it’s very important to take precaution to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on wen based heart disease prediction technique based on various medical attributes. Heart disease prediction system were prepared to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and Naïve Bayes to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using Naïve Bayes and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the patients with heart disease. Keywords: Web Based, Heart, Disease, Prediction Model, Machine Learning.