Heart Disease Binary and Multiclass Classification Using Deep Learning Hybridized with Ensemble Learner

Abstract

With growth in world's population over the last several decades, the provision of medical care has emerged as the component of human life that is most essential. A considerable number of people are losing their lives much too early due to heart disease on an annual basis. It is crucial to detect the illness at its earliest possible stage in order to lessen the chance of dying from heart disease. This should be done as soon as possible. The enormous amounts of information generated for diagnostic purposes have made it possible to construct complex learning-based models for the early, automated diagnosis of cardiac issues. This has been made possible as a result of the availability of this information. Because of this, there have been substantial gains made in the accuracy of diagnostics. As a consequence of this, there have been significant breakthroughs made in medical technology. The traditional approaches to machine learning are unable to generalize their conclusions to new datasets since these datasets were not part of the training set. As a direct result of this, the trained model's ability to provide accurate forecasts is being negatively impacted as a direct consequence of this. This research suggests a Deep learning based optimum gradient non-linear mapping of features in addition to learning through bagging and boosting-based ensemble learning in order to improve the accuracy of detecting five distinct types and binary classification of heart disease. considerable enhancement in accuracy from 8-10% and in binary average 2-3%" indicates that the use of deep learning approaches like ResNet-50 and CNNs has led to a significant improvement in the model's classification performance. The 8-10% enhancement might refer to a multi-class classification problem where the accuracy metric is generally lower due to the increased complexity of the task, while the 2-3% improvement could relate to a binary classification task where accuracies are usually higher, and even small improvements can be hard to achieve and thus are quite meaningful.