An Efficient Method for Heart Failure Diagnosis

The primary objective of this research paper is to develop an efficient method for the early identification of heart failure. Two classification techniques—Logistic Regression (LR) and Naive Bayes (NB)—were used in a series of experiments utilizing the heart failure dataset from the UCI repository. The authors selected accuracy as the performance metric and applied the robust feature selection technique to both classifiers. The experimental approach systematically excluded each prediction attribute and calculated accuracy with the remaining features. Notably, when the Platelets feature was removed, both LR and NB obtained a remarkable 100% accuracy. This significant finding highlights the potential of the suggested model for the early detection of heart failure. This research gives priceless insights that might empower doctors in improving heart failure diagnosis and patient care by identifying critical predictors. The proposed model shows potential for medical practitioners engaged in diagnosing heart failure.