Comparison of Some Machine Learning Algorithms for Predicting Heart Failure

In this modern era, people are working hard to meet their physical needs and non-effective their ability to spend time for themselves which leads to physical stress and mental disorder. Many reports state that heart failure is caused by many diseases that we ignore and chronic diseases as well as the global epidemic of the Coronavirus. Heart failure does not mean that it will stop at any moment but rather that the heart is not working as it should. Heart failure, also known as congestive heart failure, is a condition that develops when your heart does not pump enough blood for your body’s needs. This paper aims to predict if someone is at high risk of being diagnosed as a heart patient using different machine learning methods. We have collected datasets to analyze data and mining using 7 algorithms of machine learning to predict whether the patient suffers from heart failure or not. This paper used a dataset retrieved from kaggle repository, which consists of 12 attributes (Features). This work is implemented using K-Nearest Neighbors (KNN), Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (TD) and Neural Network (NN) algorithms. Results showed that Logistic Regression, Support Vector Machine and Neural Network respectively gave the best result with an accuracy of up to 94.57%.