Techniques of Machine Learning for Detecting Heart Failure

Abstract

Heart failure is a major health problem affecting millions of people worldwide. Early detection of heart failure is crucial to ensure timely treatment and reduce the risk of complications. Machine learning approaches have shown promise in detecting heart failure at an early stage. In this study, we aimed to develop a machine learning-based approach for early detection of heart failure using clinical and laboratory data collected from secondary data source in Kaggle. We used a supervised learning approach to develop a predictive model for heart failure. Six machine learning techniques, including logistic regression, decision trees, Random Forest, support vector machines, K-Nearest Neighbor, and Naive Bayes, had their performance evaluated. We have compared the performance of these algorithms to more well-known risk prediction models that only depend on demographic and medical information. Our results showed that the random forest algorithm had the best performance in detecting heart failure, with an accuracy of 86.23%, precision of 86.19%, recall of 86.23%, and F1 score 86.19%. In conclusion, our study demonstrated the potential of machine learning approaches for early detection of heart failure using clinical and laboratory data. The developed model has the potential to be used as a screening tool for early detection of heart failure, which could lead to improved outcomes for patients.