Bio-Inspired PSO for Improving Neural Based Diabetes Prediction System

Authors

  • Mohammad Zubair Khan Department of Computer Science and Information, Taibah University, Medina, Saudi Arabia https://orcid.org/0000-0002-2409-7172
  • R. Mangayarkarasi School of Information Technology and Engineering, VIT University, Vellore Campus, India
  • C. Vanmathi School of Information Technology and Engineering, VIT University, Vellore Campus, India
  • M. Angulakshmi School of Information Technology and Engineering, VIT University, Vellore Campus, India https://orcid.org/0000-0002-7712-1746

DOI:

https://doi.org/10.13052/jicts2245-800X.1025

Keywords:

F-Score, PSO, neural-network, hybrid-feature-selection, Machine learning;, diabetes-dataset

Abstract

A high level of glucose in the blood over a long period creates diabetes disease. Undiagnosed diabetes may trigger other complications such as cardiovascular disease, nerve damage, renal failure, and so on. There are many factors age, blood pressure, food habits, lifestyle changes are some of the reasons for diabetes. With increasing cases of diabetes in the smart Internet world, there is a need for an automated prediction system to facilitate the patients, to get know, whether they are affected by the disease or not. There are many diabetes prediction software that is already in use, still, the accurateness of a diabetes prediction is not complete. This paper presents a robust framework (PSO-NNDP), employs a novel hybrid feature selector to improvise the neural-based diabetes prediction system. The novel hybrid feature selector presented in this paper comprises the merits of the correlation coefficient, F-score, and particle swarm optimization methods to influence the feature selection process. The reliability of the proposed framework has been experimented on the benchmarking dataset. By establishing the clear steps, for the replacement of missing values, removal of outliers, the proposed framework obtains 99.5% accuracy. Moreover, the experimented machine learning models also show a great improvement upon the usage of the proposed feature selector.

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Author Biographies

Mohammad Zubair Khan, Department of Computer Science and Information, Taibah University, Medina, Saudi Arabia

Mohammad Zubair Khan received the Master and the Ph.D. degree in computer science and information technology from the Faculty of Engineering, M. J. P. Rohilkhand University, Bareilly, India. He was the Head and an Associate Professor with the Department of Computer Science and Engineering, Invertis University, Bareilly. He has more than 15 years of teaching and research experience. He is currently an Associate Professor with the Department of Computer Science, Taibah University. He has published more than 70 journals and conference papers. His current research interests include data mining, big data, parallel and distributed computing, theory of computations, and computer networks. He has been a member of the Computer Society of India since 2004.

R. Mangayarkarasi, School of Information Technology and Engineering, VIT University, Vellore Campus, India

R. Mangayarkarasi received her Ph.D. Degree in Information Technology and Engineering from VIT University, M.E. Computer Science from Anna University. She is working as an Associate Professor in the School of Information Technology and Engineering at VIT University, Vellore Campus, India. She is having 19 years of teaching and research experience. Her research interest includes Computer Vision, Image Processing, Machine Learning, Deep Learning, and the Internet of Things.

C. Vanmathi, School of Information Technology and Engineering, VIT University, Vellore Campus, India

C. Vanmathi received her Ph.D. degree in Information Technology and Engineering from VIT University, M.Tech (IT) from Sathyabama University, and B.E. Computer Science from Madras University. She is working as an Associate Professor in the School of Information Technology at VIT University, Vellore Campus, India. She is having 17 years of teaching and research experience. Her area of research includes Deep Learning, Computer Vision, Soft Computing, Cyber-Physical Systems, and the Internet of Things. She is a member of Computer Society of India and Soft Computing Research Society.

M. Angulakshmi, School of Information Technology and Engineering, VIT University, Vellore Campus, India

M. Angulakshmi received her Ph.D. degree in Information Technology and Engineering from VIT University, M.E (CSE) from Anna University, and B.E. Computer Science from Bharathithasan University. She is working as an Assistant Professor in the School of Information Technology at VIT University, Vellore Campus, India. She is having 14 years of teaching and research experience. Her area of research includes Image Processing, Machine Learning, and Deep Learning. She is a member of Computer society of India.

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Published

2022-05-07

Issue

Section

Intelligent Systems for Smart Applications