A Computer-Aided Diagnosis for Cardiovascular and Hepatic Disorders using Boosted Ensemble Deep Learning

Abstract

In recent years, machine learning has gained traction as a potential tool for improving the accuracy and timeliness of illness diagnoses. The use of machine learning for the diagnosis of cardiovascular and renal disorders is critically examined in this research. To enhance patient outcomes, it is essential to diagnose cardiovascular and hepatic illnesses early and accurately. The interpretation of complicated clinical data and the identification of detailed patterns indicative of these disorders, however, may be difficult for standard diagnostic approaches. This study thoroughly tests three cutting-edge boosting algorithms: XGBoost (Extreme Gradient Boosting), CatBoost, and AdaBoost. Enabling the capture of complex nonlinear interactions and management of varied data sources, these ensemble approaches repeatedly integrate and optimize numerous weak learners. After conducting thorough experiments on massive clinical datasets, results show that the XGBoost algorithm is the best for certain types of diseases. Intelligent diagnostic tools that can reliably identify cardiovascular and hepatic diseases early on are within reach, according to the results of this study. This will lead to better patient care and disease management methods.