Paper Key : IRJ************838
Author: Shaik Reenaz Begum,Shaik Mohammed Nafeel,Shaik Mahaboob Basha,Shaik Sofiya
Date Published: 24 Mar 2025
Abstract
Heart disease remains one of the most significantglobal health challenges, accounting for a substantial proportionof morbidity and mortality worldwide. Early detection and timelyintervention are critical in reducing complications, improvingpatient outcomes, and minimizing the overall healthcare burden.Traditional diagnostic methods often rely on clinical expertise,manual analysis, and conventional statistical approaches, whichmay be limited in terms of predictive accuracy and scalability.In recent years, the advancement of machine learning (ML)techniques has demonstrated significant potential in improvingdisease prediction and medical diagnosis. This study aims toexplore and evaluate various ML algorithms in predicting heartdisease, leveraging patient medical data. The research employsthe UCI Heart Disease dataset, a widely used benchmark datasetin medical predictive analytics, containing multiple clinical parameters such as age, sex, cholesterol levels, blood pressure,electrocardiogram (ECG) results, and other relevant indicators.Several ML models, including logistic regression (LR), decisiontrees (DT), random forests (RF), support vector machines (SVM),k-nearest neighbors (KNN), and artificial neural networks (ANN),were implemented and assessed for their predictive performance.Key performance metrics such as accuracy, precision, recall,F1-score, and area under the receiver operating characteristiccurve (AUC-ROC) were used to compare the effectiveness of eachmodel. Hyperparameter tuning and feature selection techniqueswere also applied to optimize the predictive capability of thesealgorithms.The findings indicate that ML-based predictive models significantly enhance the accuracy of heart disease diagnosis comparedto traditional methods. Among the evaluated models, certain techniques exhibited superior performance in terms of classificationaccuracy and generalization ability. The study further discussesthe interpretability of ML models and the potential integrationof these predictive systems into clinical decision support tools,enabling healthcare professionals to make data-driven diagnosesand initiate early intervention strategies.By leveraging ML-based predictive analytics, this research contributes to the ongoing efforts in advancing healthcare technologyand improving patient care. Future work may focus on expandingthe dataset, incorporating deep learning methodologies, andexploring real-time predictive applications in clinical settings.