Modelling, simulation, and optimization of diabetes type II prediction using deep extreme learning machine

Article type: Research Article

Authors: Rehman, Abdur^a | Athar, Atifa^b | Khan, Muhammad Adnan^{a; c; *} | Abbas, Sagheer^a | Fatima, Areej^a | Atta-ur-Rahman, ^d | Saeed, Anwaar^e

Affiliations: [a] Department of Computer Science, National College of Business Administration & Economics, Lahore, Pakistan | [b] Department of Computer Science, Comsats University Islamabad, Lahore Campus, Pakistan | [c] Department of Computer Science, Lahore Garrison University, Lahore, Pakistan | [d] Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia | [e] Department of Computer Science, Virtual University, Islamabad, Pakistan

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: Diabetes is among the most common medical issues which people are facing nowadays. It may cause physical incapacity or even death in some cases. It has two core types, namely type I and type II. Both types are chronic and influence the functions of the human body that regulate blood sugar. In the human body, glucose is the main element that boosts cells. However, insulin is a key that enters the cells to control blood sugar. People with diabetes type I do not have the ability to produce insulin. Whereas people with diabetes type II lack the ability to react to insulin and frequently do not make enough insulin. For adequate analysis of such a fatal disease, techniques with a minimum error rate must be utilized. In this regard, different models of artificial neural network (ANN) have been investigated in the literature to diagnose/predict the condition with a minimum error rate, however, there is a need for improvement. To further advance the accuracy, a deep extreme learning machine (DELM) based prediction model is proposed and investigated in this research. By using the DELM approach, a high level of reliability with a minimum error rate is achieved. The approach shows significant improvement in results compared to previous investigations. It is observed that during the investigation the proposed approach has the highest accuracy rate of 92.8% with 70% of training (9500 samples) and 30% of test and validation (4500 examples). Simulation results validate the prediction effectiveness of the proposed scheme.

Keywords: DELM, ANN, feedforward, backpropagation algorithm, diabetes prediction

DOI: 10.3233/AIS-200554

Journal: Journal of Ambient Intelligence and Smart Environments, vol. 12, no. 2, pp. 125-138, 2020