An Applied Artificial Intelligence Technique For Early Prediction of Diabetes Disease

Abstract

Diabetes Mellitus is a common issue all over the world. There are many cases of diabetes that are recorded on daily bases. This chronic disease takes the entire life of a patient to recover from his health. The doctor can save it only in the early stages of the disease because if it is the last stage, it would be almost impossible for patients to recover from diabetes. The system is proposed to provide the best solution for the early prediction of diabetes. So that the doctor detects the disease in an early stage. The main purpose of research on early diabetes is to go ahead and improve the detection system of diabetes in the model for doctors to predict the patient's disease efficiently. Many research papers are public about different types to predict in different stages. But this work aims to predict the symptom in early-stage to stop the disease from the root so that it would not cause harm in the future. If the problem is solved at the early stage of the disease, it will be easy for patients to recover easily saving them from a big loss in the future. This research applied severer Machine Learning and Deep Learning algorithm to check the model's performance. In return, K Nearest Neighbor and Decision Tree provide the best accuracy in the applied dataset. The highest accuracy KNN model got 93.66% and the Decision Tree got 90.10% accuracy. KNN is the best algorithm in this case which provides good accuracy for the provided dataset. The Dataset used in this research was built by a Bangladesh hospital with a direct survey form with different patients. It has 17 attributes and a collection of 520 instances. The KNN algorithm is handy and has got 93.66% accuracy to apply the medical professional treatment.