Enhancing Accuracy & Efficiency Analysis for Heart Disease Prediction using Machine Learning Algorithm

Abstract

Cardiovascular diseases remain a leading cause of mortality worldwide, emphasizing the importance of accurate and efficient methods for early detection and prediction. This study focuses on enhancing the accuracy and efficiency of heart disease prediction by employing advanced machine learning algorithms. Traditional risk assessment methods often lack the precision required for early intervention, making the integration of machine learning an essential component in modern healthcare. The proposed research leverages a comprehensive dataset containing diverse patient information, including demographic details, lifestyle factors, and medical history. A multi-faceted approach combines feature engineering, data preprocessing techniques, and state-of-the-art machine learning algorithms to develop a robust predictive model.

To enhance accuracy, the study explores ensemble learning techniques, including Random Forests, Gradient Boosting, and stacking methods. The ensemble approach leverages the strengths of individual models, resulting in a more robust and accurate prediction model. Furthermore, deep learning algorithms such as neural networks are employed to capture intricate patterns and dependencies within the data. Efficiency is addressed through optimization strategies, including model hyper parameter tuning and dimensionality reduction techniques. The goal is to streamline the computational requirements without compromising predictive performance. The study investigates the trade-offs between model complexity and efficiency, ensuring practical implementation in real-world healthcare settings. The performance of the proposed model is rigorously evaluated using cross-validation techniques and compared against existing heart disease prediction models.

The outcomes of this research have the potential to revolutionize heart disease prediction, offering healthcare practitioners a reliable tool for early identification of at-risk individuals. By leveraging the power of advanced machine learning algorithms, this study contributes to the on-going efforts to enhance preventive healthcare strategies and reduce the global burden of cardiovascular diseases.