Retracted: Ensemble Machine Learning (Grid Search & Random Forest) based Enhanced Medical Expert Recommendation System for Diabetes Mellitus Prediction
P. Nagaraj, V. Muneeswaran, K. Muthamil Sudar, Naga Vardhan Reddy A, G Deshik, Charan Kumar Reddy C
Abstract
"Araetus" was the person who coined the term "Diabetes" and later on the word "mellitus" which means honey-sweet was added to it by Thomas Willis in the year 1675 after noticing the sweet nature of the blood and urine of the patients. Currently, it is one of the most developing lethal maladies throughout the world to mankind. The number of diabetes people being reported is increasing every day and also annually it costs a lot of money to treat people who are suffering from diabetes mellitus (DM). So initially, the idea of creating a system that is capable of predicting the type of diabetes, and recommending diet, the quantity of insulin to be taken, and the types of physical workouts for the user was only focussed. Later on, this system is designed to propose the benefits for diabetic patients which help them to fight their type of diabetes. This system can make personalized recommendations based on their type and status of diabetes. Here, an ensemble machine learning algorithm is used to predict whether a person is diabetic or not and what type of diabetes the person has. Diabetes mellitus is characterized by pancreatic insufficiency and an increased blood sugar level. As a result, a person with diabetes must be saved by controlling the difficulties of DM. To successfully forecast the presence of DM and to lessen the severity of DM outcomes, a predictive model is necessary. The primary goal of this work is to create and develop a DM prediction model that uses machine learning techniques to operate as a decision-making system to predict and recommend.