A Deep Learning Framework for Prediction of Heart Diseases from Clinical Observations

Among today's youth, heart disease has emerged as a major health concern. Many forms of cardiac disease can be traced back to bad habits and clinical characteristics. Many other models have been proposed by researchers to aid in the diagnosis of various conditions, however the vast majority either has poor accuracy performance or are overly complex to implement in real-time clinical settings. In addition, these models tend to function correctly for smaller class sets, but their performance degrades with an increase in class count. To facilitate answer these issues, this study suggests creating a novel, effective, and extensible strategy for disease prediction. This employs a deep learning technique, which boosts multiple sets of clinical data using a combination of StackingCV classifier-bagging and feature-boosting. Feature variance is increased by augmenting these vectors with a boosting of classifiers at the outset. AdaBoost strategy (ABA), Support-Vecto-Classifier (SVC), and Extreme-Gradient-Boosting (XGBoost) are then applied to these characteristics to help improve accuracy performance at the cost of minuscule increases in computational delays. Single illness classifier accuracy was 93.44% and multiple disease classifier accuracy was 60.5% when the model was tested using datasets from the University of California, Cleveland. When contrasted to various state-of-the-art methods and classifiers, such as a Deep-Neural-Network (DNN), Random-Forest (RF), XGBoost, &SVC, the suggested model was shown to be at least 3.9% more accurate. Thus, it is appropriate for use in live medical settings.