An Automated Machine Learning Approach for Detecting Chronic Ischemic Heart Disease

A major global health concern is Chronic Ischemic Heart Disease (CIHD), which needs prompt and accurate diagnosis for efficient treatment. To improve the diagnosis accuracy of CIHD, this study investigates the use of an Automated Machine Learning (AutoML) technique, utilising the Tree-based Pipeline Optimisation Tool (TPOT). The study's conclusions provide insightful information about how TPOT AutoML might enhance CIHD diagnosis. Healthcare professionals can benefit from the automated approach's user-friendliness and time efficiency as well as its competitive diagnostic accuracy. It is possible to improve CIHD diagnosis by the use of such sophisticated automated machine learning algorithms in clinical settings, which will ultimately lead to better patient care and treatment.