Enhancing Heart Disease Risk Prediction Accuracy through Ensemble Classification Techniques
Keywords:
Machine Learning, Ensemble method, heart disease, Classification Techniques, prediction modelAbstract
A crucial part of handling different data science problems is machine learning, a branch of artificial intelligence. One of its common uses is making predictions based on past data. Classification is a powerful machine learning technique that is frequently used to produce accurate predictions. On the other hand, some categorization algorithms may have a maximum level of accuracy. In this study, the ensemble classification technique which combines multiple classifiers is investigated as a means of enhancing the precision of less accurate algorithms. Accurate diagnosis and classification of cardiovascular diseases are essential for selecting the most effective treatment and reducing mortality. Machine learning has developed into a crucial tool in the medical sector by leveraging data patterns for improved diagnosis. This project aims to reduce misdiagnosis and improve patient outcomes by developing a predictive model for cardiovascular illnesses using machine learning techniques. In this study, machine learning is used to address the problem of accurate classification of cardiovascular diseases. The developed methodology can help diagnosticians make informed choices that lead to quick and targeted therapies. This study shows how machine learning has a significant impact on medicine and has the potential to reduce cardiovascular disease-related mortality. On a dataset of heart disease patients, the work focuses on employing ensemble classification to increase prediction accuracy. The goal is to demonstrate the algorithm's value in predicting diseases early using medical data and to raise the accuracy of weaker classifiers. Experimental comparisons were done to determine the impact of the ensemble technique on the accuracy of heart disease prediction.
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