Academy & Industry Research Collaboration Center (AIRCC)

Volume 11, Number 03, March 2021

Towards Comparing Machine Learning Models to Foresee the Stages for heart disease


Khalid Amen, Mohamed Zohdy and Mohammed Mahmoud, Oakland University, USA


With the increase in heart disease rates at advanced ages, we need to put a high quality algorithm in place to be able to predict the presence of heart disease at an early stage and thus, prevent it. Previous Machine Learning approaches were used to predict whether patients have heart disease. The purpose of this work is to compare two more algorithms (NB, KNN) to our previous work [1] to predict the five stages of heart disease starting from no disease, stage 1, stage 2, stage 3 and advanced condition, or severe heart disease. We found that the LR algorithm performs better compared to the other two algorithms. The experiment results show that LR performs the best with an accuracy of 82%, followed by NB with an accuracy of 79% when all three classifiers are compared and evaluated for performance based on accuracy, precision, recall and F measure.


Machine Learning (ML), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbors (KNN).