AI-Driven Heart Disease Prediction Using Machine Learning and Deep Learning Techniques
DOI:
https://doi.org/10.22399/ijcesen.1669Keywords:
Heart Disease Prediction, Machine Learning Deep Learning, XGBoost, AI in HealthcareAbstract
Heart disease remains a leading cause of mortality worldwide, necessitating early detection and prevention strategies. This study explores machine learning (ML) approaches for predicting heart disease using patient datasets. Various ML algorithms, including Logistic Regression, Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, XGBoost, and an Artificial Neural Network (ANN), were implemented to classify heart disease presence. The Random Forest model achieved the highest accuracy of 95%. The findings demonstrate that ML can significantly enhance heart disease prediction, aiding early diagnosis and treatment.
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