Reassessing Addison’s: Engineering Errors in Diagnosis and Ranges

Addison’s disease is a medical condition that occurs when the adrenal glands that are located just above the kidneys are damaged which results in low production of the hormone cortisol and in certain instances Aldosterone. Therefore, it is of crucial importance that we detect Addison’s disease at an early stage so that it will improve the standard of living for the patients and also reduce complications. The use of Machine learning for diagnosing any disease is very common but while doing the same for a rare disease like Addison’s, certain errors pop up which affects the final diagnosis of it. Therefore, In this paper, an Ensemble model is designed named Ensemble Cast which is based on 10 input parameters that are very important for the diagnosis of AD. This model consists of five base learners that provide their individual decisions which are then categorized and then a system of voting is performed after which the class with majority votes is declared as the final output.