A random forest algorithm achieved the best performance in heart failure severity evaluation and type prediction compared to neural networks, support vector machines, and other models.
Does a random forest-based clinical decision support system improve heart failure severity evaluation and type prediction compared to other machine learning algorithms?
A random forest-based clinical decision support system provides superior performance for evaluating heart failure severity and predicting heart failure type compared to other machine learning models.
In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups. The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use. To implement the smart intelligent functions, we adopted a machine learning approach. In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database. Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm. The management tool allows the cardiologist to populate a "supervised database" suitable for machine learning during his or her regular outpatient consultations. The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case.
Guidi et al. (Thu,) conducted a other in Heart failure. Machine learning clinical decision support system vs. Neural network, support vector machine, fuzzy rules, classification and regression tree was evaluated on Heart failure severity evaluation and type prediction. A random forest algorithm achieved the best performance in heart failure severity evaluation and type prediction compared to neural networks, support vector machines, and other models.