Enhanced Machine Learning Model for Heart Disease Prediction

Heart disease remains one of the leading causes of mortality worldwide, highlighting the critical need for effective prediction and early detection methods. This paper proposes a hybrid heart disease prediction model that integrates three machine learning techniques: Linear Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). The hybrid approach leverages the strengths of each algorithm to improve the accuracy and robustness of predictions. Linear Regression is employed for its ability to model the relationship between risk factors and heart disease in a straightforward and interpretable way, providing baseline predictions. Support Vector Machine contributes by optimizing decision boundaries and handling non-linearities in the dataset, ensuring better classification in complex cases. Finally, Random Forest, an ensemble learning method, enhances prediction accuracy by reducing variance and avoiding overfitting, making it suitable for handling high-dimensional and noisy medical data. The proposed hybrid model is evaluated using a heart disease dataset, and its performance is compared against standalone algorithms. Key metrics such as accuracy, precision, recall, and F1-score are used to assess the effectiveness of the model. Experimental results demonstrate that the hybrid approach significantly improves prediction performance, achieving higher accuracy and lower false positive and false negative rates compared to individual models.