Predicting The Classification Of Heart Failure Patients Using Optimized Machine Learning Algorithms

Abstract

Heart failure is one of the leading causes of mortality worldwide, and early identification of patients at risk is essential for improving survival rates. Traditional diagnosis methods rely heavily on clinical expertise and manual interpretation of medical data, which can be time-consuming and prone to human error. Existing systems mainly use conventional statistical techniques that lack accuracy, scalability, and transparency in decision-making. To address these challenges, this project proposes a machine learning–based approach for classifying heart failure patients using optimized algorithms. Patient clinical data is processed and analyzed using advanced machine learning models such as Logistic Regression, Random Forest, and XGBoost to improve prediction accuracy. In addition, explainable artificial intelligence techniques are incorporated to provide clear and understandable explanations for each prediction. This helps medical professionals trust and interpret the model’s decisions effectively. The proposed system aims to assist healthcare professionals by providing accurate, fast, and interpretable predictions, thereby supporting better clinical decision-making and improving patient outcomes. Keywords: Heart Failure Prediction, Machine Learning, XGBoost, Explainable AI.