Early Stage Diabetes Likelihood Prediction using Artificial Neural Networks
2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)
(2020), pp. 1-5
Rex Paolo C. Gamara
a
,
Argel A. Bandala
b
,
Pocholo James M. Loresco
a
,
Ryan Rhay P. Vicerra
c
a FEU Institute of Technology, Electrical and Electronics Engineering Department, Manila, Philippines
b De La Salle University, Electronics and Communications Engineering Department, Manila, Philippines
c De La Salle University, Manufacturing Engineering and Management Department, Manila, Philippines
Abstract: Diabetes is a disease which chronic in nature, which is caused by an elevated blood sugar (or blood glucose) level. The metabolic disease is linked to several potential serious organ complications including nerves, kidneys, eyes, blood vessels, and the heart. According to the International Diabetes Federation, in 2019, about 2 million deaths were recorded worldwide due to diabetes. Furthermore, according to Philippine Statistics Authority (PSA), Diabetes Mellitus is considered as the fifth main cause of in the Philippines in the past years and in a 2015 study, about 1.7 million Filipinos are still undiagnosed of diabetes. Therefore, several machine learning-based techniques were developed for diabetes risk prediction. However, these works have yet to utilize artificial neural networks using the symptom information of suspected diabetic patients. This research paper demonstrated an ANN-based diabetes risk classification based on the symptom information of patients. The scaled conjugate gradient backpropagation technique was utilized for neural network training process. The classification system showed 99.2% overall correctness in determining the likelihood of diabetes.