Heart disease prediction using machine learning techniques

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This presentation explores heart disease detection through machine learning. It begins by addressing the significance of early intervention and technological advancements. Core ML methods, including KNN, SVM, DT, and RF, are compared, with a focus on the practical advantages of RF. Using the Cleveland dataset, it examines preprocessing for balanced, quality data. The methodology simplifies features into a binary class, driving efficient predictive models. Results emphasise RF’s accuracy and...