Abstract:
Heart disease is getting increasingly widespread, and it has a high fatality rate
throughout the world. Heart disease has become a major health concern for many
individuals and the leading cause of mortality worldwide in the previous decade. This
is a challenging process that must be completed accurately and effectively. The study
report focuses on which people are more prone to acquire heart disease depending on a
variety of medical factors. We created a heart disease prediction method based on the
patient's medical history that predicts whether the patient is likely to be diagnosed with
a heart disease or not. The research title is "Heart Disease Prediction Using Machine
Learning" and it focuses on the prediction of heart disease as well as showing who is
impacted by heart disease and who is not based on the patient's medical data. Machine
learning may provide an effective decision-making solution as well as precise forecasts.
In the medical field, machine learning techniques are commonly used. Researchers
favor models based on supervised learning techniques such as Support Vector
Machines (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees
(DT), and ensemble models.
