A Review on Early Prediction of Heart Failure Using Machine Learning Models

Many individuals suffer from heart failure, which is a recurrent issue worldwide. Early detection of this illness is crucial in determining the best course of action. Advising early prediction of heart failure can help prevent life-threatening situations. In recent times, machine learning algorithms have been utilized to forecast diverse types of ailments. Machine learning models are trained using datasets and tested using parameters that have been medically verified. Numerous researchers have put forth various approaches utilizing machine learning (ML) for early detection based on a few metrics, such as age, gender, blood pressure, BMI, diabetes, Hb level, and many more. The goal of our study is to compare various existing ML models considering the categories like Accuracy, F1 Score, Recall and Precision. To predict the early detection of heart failure, the following ML Algorithms are considered, evaluated, and compared in the study: Random Forest Classifier (RFC), Naive Bayes, XGBoost, K Nearest Neighbour (KNN), and Logistic Regression (LR). These ML algorithms are implemented in various data sources, and their performance is closely monitored. According to the findings, Random Forest Classifier (RFC) is preferred for prediction.