Abstract:

Diabetes among one of the most common diseases occurs in human beings due to imbalance of insulin level in blood. The early detection of diabetes is very necessary as it can affect many internal parts and immune system of human body silently. If we take proper precautions on the early stage, it is possible to take control of diabetes disease. This paper presents, An Ensemble Model for early prediction of Typical and Non- Typical Diabetes Disease. PIMA Indians Diabetes Database which is obtained from UCI repository is used as input dataset. The dataset has two types of symptoms Typical and Non-typical. An ensemble model which the combination of four classifications as SVM (Support Vector Machine), Decision Tree (DT), RF (Random Forest) and Naïve Bayes (NB). The proposed ensemble model gives the best results for diabetic prediction and the result showed that the prediction system is capable of predicting the diabetes disease effectively, and efficiently in terms of Accuracy and Precision parameters

Description:

diabetes disease prediction, machine learning, SVM, NB, DT, RF, Typical, Non-typical