Design of Gradient Boosting Ensemble Classifier with Variation of Learning Rate for Automated Cardiac Data Classification

Cardiac data classification is an emerging research area in recent days. Machine learning-based automatic classification model is one of the essential aspects for the diagnosis of cardiac disease. The performance of a model can be improved by combining multiple models to solve a single problem. In this work, the authors have adopted a modified gradient boosting ensemble learning-based classifier for classifying the cardiac data collected from the UCI machine learning repository. The data set contains the samples of 303 patients with 13 attributes related to cardiac symptoms. The classification is done by using two types of gradient boosting ensemble classifier. In the first step, the classification is performed with a fixed learning rate of 0.01 for every tree. Further to improve the performance the learning rate is changed for each tree. From the result, it is observed that the accuracy is increasing with variation in learning rate. 91% accuracy is observed while the learning rate of 0.81 is considered. The performance is compared with the earlier works and is observed that the proposed model is providing a better result.