Nowadays, machine learning is growing fast to be more popular in the world,
especially in the healthcare field. Heart diseases are one of the most fatal diseases,
and an early prediction of such disease is a vital task for many medical
professionals to save their patient’s life. The main contribution of this research
is to provide a comparative analysis of different machine learning models
to reach the most supporting decision for diagnosing heart disease with
better accuracy as compared to existing models. Five models namely, K-Nearest
Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), Support
Vector Machine (SVM), and Extreme Gradient Boost (XGB), have been introduced
for this purpose. Their performance has been tested and compared
considering different metrics for precise evaluation. The comparative study
has proven that the XGB is the most suitable model due to its superior prediction
capability to other models with an accuracy of 91.6% and 100% on
two different heart ailments datasets, respectively. Both datasets were acquired
from the heart diseases repositories where dataset_1 was taken from the University
of California, Irvine (UCI) and dataset_2 was from Kaggle.