Supervised Learning Approaches on the Prediction of Diabetic Disease in Healthcare

There are many chronic diseases out of which Diabetes is one; that increases sugar level in the blood and is one of the most fatal that effect different organs in the human body. Diabetes can cause a variety of slow bad consequences if not detected and left without given medical care. The emergence of machine learning approaches, on the other hand, solves this crucial issue. The purpose and objectives of this work is to build a prototypical model that can properly forecast diabetes whether or not a person will suffer from it. To detect diabetes at an early stage, our work employs three classification algorithms based on supervised learning: Random Forest, Naïve Bayes Classifier and Multilayer Perceptron Network. The PIDD Database has been used in the experiments. The Precision, Accuracy, Recall, F-Measure, and ROC Area are all used to calculate the efficiency of the above three algorithms. The correctness and accuracy of a classification system is measured by the number of occurrences that are correctly classified and those that are mistakenly classified.