Predicting the Progression of Health Affecting Disease: A Comparative Analysis using ANN and Ensemble Approach

Many of the available machine learning models for health care analysis focus on a single disease at a time. For example, one analysis could be for diabetes another for thyroid disease, and another for brain stroke conditions. There is no standard system in which a single analysis may forecast more than one disease. Diseases such as chronic kidney disease, brain stroke, diabetes, stroke, and thyroid disease kill many people worldwide, however the majority of these deaths are caused by a lack of early disease screenings. This study’s main objective is to provide an approach for decreasing the dimensionality of medicinal datasets by eliminating unnecessary and redundant characteristics, hence improving classification accuracy while decreasing computing time. The existing system using machine learning algorithms provides less accuracy when predicting Multiple diseases. The essential point is to choose an acceptable threshold for removing unnecessary and duplicated information from the collection. The objective of the decision-making approach is also promoted. The existing model analyzed in the chronic kidney disease dataset. This paper introduces Multiple disease prediction system with deep learning techniques like Artificial Neural Network. This model acceptance various disease datasets and predicting disorder Once an illness has been diagnosed and shown to afflict a patient, both the patient and the carers, including physicians, must adhere to certain guidelines. It is therefore feasible to halt illness development in the patient. Using this model, the disease can be recognized at the early stage itself, and cure the disease, otherwise, it will be difficult to treat later.