Abstract: Diabetes is a disease that is chronic. Improper blood glucose control may cause serious complications in diabetic patients as heart and kidney disease, strokes, and blindness. Obesity is considered to be a massive risk factor of type 2 diabetes. Machine Learning has been applied to many medical health aspects. In this paper, two machine learning techniques were applied; Support Vector Machine (SVM) and Artificial Neural Network (ANN) to predict diabetes mellitus. The proposed techniques were applied on a real dataset from Al-Kasr Al-Aini Hospital in Giza, Egypt. The models were examined using four-fold cross validation. The results were conducted from two phases in which forecasting patients with fatty liver disease using Support Vector Machine in the first phase reached the highest accuracy of 95% when applied on 8 attributes. Then, Artificial Neural Network technique to predict diabetic patients were applied on the output of phase 1 and another different 8 attributes to predict non-diabetic, pre-diabetic and diabetic patients with accuracy of 86.6%.

Raghda Essam Ali, Hatem El-Kadi, Soha Safwat Labib and Yasmine Ibrahim Saad, “Prediction of Potential-Diabetic Obese-Patients using Machine Learning Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 10(8), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100812

@article{Ali2019,
title = {Prediction of Potential-Diabetic Obese-Patients using Machine Learning Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100812},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100812},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {8},
author = {Raghda Essam Ali and Hatem El-Kadi and Soha Safwat Labib and Yasmine Ibrahim Saad}
}