Heart Disease Prediction Using Machine Learning

The number of heart disease cases is rising quickly every day, making it crucial and worrisome to anticipate any such illnesses in advance. This diagnosis is a challenging task that requires accuracy and efficiency. The primary goal of the research work is to determine which patient, based on different medical parameters, is more likely to have a cardiac condition. Using the patient’s medical history, we developed a heart disease prediction algorithm to determine the likelihood of a heart disease diagnosis or not. To forecast and categorize the patient with heart disease, we employed a variety of machine learning algorithms, including logistic regression and kernel neighborhood network. A very helpful method was employed to control the model’s ability to increase any person’s heart attack prediction accuracy. In comparison to the previously utilized classifiers, such as naive bayes, etc., the proposed model’s strength was quite satisfying. It could predict signs of heart disease in a specific individual by utilizing KNN and Logistic Regression, which demonstrated a good accuracy. Thus, by utilizing the provided model to determine the likelihood that the classifier can correctly and precisely diagnose cardiac illness, a sizable amount of pressure has been released. The Given heart disease prediction system lowers costs and improves medical care. This research provides us with important information that can be used to forecast the patients who will have heart disease. It is used with the.pynb file format.