Improving accuracy in intelligent coronary heart disease diagnosis prediction model using support vector clustering technique compared over Xgboost classifier algorithm.

The goal of this project is to use support vector clustering to enhance the performance of a coronary heart disease detection model in comparison to the aXgboost Classifier. Substances and Techniques: The Support Vector Clustering Method and Xgboost Classifier Algorithms are used for cardiac disease prediction. In this study, we split our participants evenly into two groups of five. Identification and forecasting are performed using a cardiac data set. There are 14 indicators in the dataset that could be used to determine if a patient has cardiac problems. Results: Two statistically reliable classifiers were used to produce the results: The observed significance level for accuracy is 0.002 (two-tailed) (p0.05). When it comes to cardiac disease prediction, Support Vector Clustering Technique appears to far outperform Xgboost Classifier. The xgboost Classifier Algorithm has an accuracy of 79.60 percent, while the Support Vector Clustering Method achieves an accuracy of 82.84 percent. [ABSTRACT FROM AUTHOR]