Heart failure prediction based on multiple machine learning algorithms

Abstract

Heart failure is a complex medical condition that arises due to the heart's inability to adequately circulate blood throughout the body, which is challenging to predict. This research aims to investigate three distinct models, namely logistic regression, random forest and decision tree generation algorithms. Logistic regression is essentially a logistic function applied to linear regression, and the loss function associated with linear regression is similar to the mean square error-like loss. In contrast, the loss function for logistic regression follows cross-entropy loss. while cross-entropy loss is often used in practice, it differs from mean square error loss. The derivative of cross-entropy loss is a difference that updates rapidly when the error is significant and slowly when the error is small, which is a desirable trait for the purposes. Decision tree generation algorithms utilize tree structures in which internal nodes represent judgments on attributes, branches represent outputs of judgments, and leaf nodes represent classification results. Random forest is an integrated learning algorithm that employs decision trees as the base learner. In classification models, multiple decision trees are processed for voting, while multiple decision tree results are processed for averaging in regression models. Experimental results indicate that random forest outperforms the other two models, albeit with a marginal difference. Further studies should incorporate additional models to identify a more suitable model for predicting heart failure.