Diabetic Disease Prediction System using Supervised Machine Learning Approaches - Ramu, Ommi; Godi, Brahmaji; Prakash Samantray, Om (International Journal of Computer Sciences and Engineering, 2021)
In the present study Diabetics is one of the critical diseases which can fall at any group of age and gender. The major causes lead to diabetics is mostly inheritance, in a proper healthy lifestyle, Irregular food habits, stress, and no physical exercise. Prediction of Diabetics is a very important study since it is one of the leading causes of sudden kidney failures, heart attacks, and brain stroke etc. The diabetic patient treatment can be done through patient health history. The Doctor can find hidden information about the patient through healthcare applications and it will be used for effective decision-making for the patient‟s health condition. The healthcare industry is also collecting a large amounts of patient health information from different data warehouses. Using these healthcare databases researchers used to extract information for predicting the diabetics of the patient. Researchers are focused on developing software with the help of machine learning methods that can help clinicians to make better decisions about a patient's health based on their prediction and diagnosis. The main purpose of this program is to diagnose a patient's diabetes using machine learning methods. A relative study of the various competences of machine learning approaches will be done through a graphical representation of the results. The goal and objective of this project is to predict the chances of diabetics then provide early treatment to patients, which will reduce the life-risk and cost of treatment. For this purpose a probability modeling and machine learning approach like Support Vector Machine algorithm Decision tree algorithm, Naive Bayes algorithm, Logistic regression algorithm are used to predict diabetics.