Cardiac Patient Data Classification Using Ensemble Machine Learning Technique

Heart disease is a major global health problem, and successful treatment and prevention depend greatly on early and correct diagnosis. Machine learning methods have showed promise in the medical data analysis and in helping to categorise diseases. In this article, we investigate the application of ensemble machine learning methods for the categorization of cardiac patient data. Three machine learning algorithms—XGBoost, SVM, and KNN are combined in our ensemble method. The robust gradient boosting method XGBoost is renowned for its capacity to manage intricate data connections. SVM is a flexible classifier that can detect boundaries in non-linear decisions. KNN is a straightforward yet powerful algorithm that uses the closeness of examples to classify data. We want to use the strengths of these algorithms by combining their predictions to make them stronger and improve categorization. The experimental findings show how well the suggested ensemble strategy for classifying cardiac patient data works. The ensemble classifier outperforms the individual classifiers with an accuracy of 88.52%. This proves that integrating the predictions of many algorithms results in a more reliable model and better classification performance. The results of this study provide a contribution to the categorization of medical data and give medical practitioners important information about identifying cardiac disease. The ensemble classifier can help in the prompt and accurate identification of patients who are at risk, allowing for early intervention and the development of effective treatment plans. The promise of ensemble machine learning algorithms in the categorization of cardiac patent data is shown by our research, in conclusion. The ensemble method using XGBoost, SVM, and KNN outperforms individual classifiers, producing a classification model that is more accurate and dependable. In order to improve the model's classification performance and interpretability, next research might concentrate on investigating other ensemble approaches and adding domain-specific knowledge.