Neuro-Fuzzy System for Heart Failure Prediction
DOI:
https://doi.org/10.29304/jqcm.2022.14.4.1084Keywords:
Heart Failure, Feature Extraction, Neuro-Fuzzy System (NFS), Artificial Neural Network (ANN)Abstract
Heart failure is one of the dangerous heart diseases that infect humans and may cause death. This disease causes damage to the heart muscle, and it becomes unable to pump blood in the body as well as it should. Therefore, the condition of heart failure patients must be predicted as soon as possible in order to help the patients to live longer lives by offering appropriate therapy. Based on that, the aims of this paper are to use medical records to predict the state of a patient with heart failure if he/she will die or not, and extract the important features that have a direct effect on the patient's state. This paper used a dataset of 299 heart failure patients and applied the Neuro-Fuzzy systems (NFS) to this dataset. This prediction is made by testing each two feature together in the dataset and feeding it to the NFS system to determine its effect on the patients. In this paper, the accuracy and confusion matrix is used to evaluate the system's performance. The experimental results show that the system yielded 100% accuracy when the two-feature, serum creatinine and ejection fraction, are tested together, so it can be used alone to predict whether patients with heart failure will survive.
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