Heart Disease Prediction using Machine Learning Algorithms

Abstract

Heart disease cases are rising quickly every day, so it's crucial and worrisome to anticipate any potential illnesses in advance. This diagnosis is a challenging job that requires accuracy and efficiency. The primary focus of the study paper is on which patients, given different medical characteristics, are more likely to have heart disease. Using the patient's medical history, we developed a method to determine whether a heart disease diagnosis is probable or not for the patient. To forecast and categorize the patient with heart disease, we used a variety of machine learning algorithms, including KNN and logistic regression. The regulation of how the model can be used to increase the precision of heart attack prediction in any person was done in a very helpful way. When compared to the previously employed classifiers, such as naive bayes, etc., the suggested model's accuracy in predicting signs of having heart disease in a specific person was quite satisfactory. It did this by using KNN and Logistic Regression. Thus, using the provided model to determine the likelihood that the classifier will correctly and reliably recognize heart disease has relieved quite a bit of pressure. Given's system for predicting heart disease improves patient treatment while costing less. This research has provided us with a wealth of information that can be used to predict who will develop heart disease. It utilizes the .pynb file type