Design of an Optimized Self-Acclimation Graded Boolean PSO with Back Propagation Model and Cuckoo Search Heuristics for Automatic Prediction of Chronic Kidney Disease

Authors

  • Anindita Khade 1) Department of Computer Engineering, Ramrao Adik Institute of Technology, DY Patil University, Nerul, Maharashtra, India 2) Department of Computer Engineering, SIES Graduate School of Technology, Nerul, Navi Mumbai Maharashtra, India https://orcid.org/0000-0003-2616-5092
  • Amarsinh V. Vidhate Department of Computer Engineering, Ramrao Adik Institute of Technology, DY Patil University, Nerul, Maharashtra, India
  • Deepali Vidhate Department of Biochemistry, School of Medicine DYPU, Nerul, Maharashtra, India

DOI:

https://doi.org/10.13052/jmm1550-4646.1962

Keywords:

CKD, prediction, Deep Learning, Back Propagation Networks, Particle Swarm Optimization, Cuckoo Search Algorithm

Abstract

Objectives: A kind of Artificial Neural Network (ANN) known as a Back Propagation Neural Network (BPNN) has been extensively applied in a variety of sectors, including medical diagnosis, optical character recognition, stock market forecasting, and others. Many studies have employed BPNN to create decision-support tools for doctors to use while making clinical diagnoses. Chronic Kidney Disease (CKD) is one such kind of disease which has been receiving due importance from the past decades due to lack of symptoms in its early stages. The goal of this work is to demonstrate the performance of Artificial Intelligent (AI) algorithms in the early detection of CKD.

Method: We received 800 patients’ real-time data from DY Patil Hospitals for this investigation. Self-Acclimation Graded Boolean PSO (SAG-BPSO), a modified version of Particle Swarm Optimization (PSO), has been proposed and used in this study to accomplish feature selection. Cuckoo Search Algorithm (CSA) has been used to optimise the weights and biases of the BPNN. Finally, this hybrid model is combined with BPNN for final predictions. Finally, a comparison is made between few state of art algorithms and the proposed approach.

Results: The accuracy noted on applying BPNN on the dataset was approximately 91.45%. The combined model of BPNN+SAGBPSO provided an accuracy of about 92.25%. The accuracy achieved for the hybrid model of BPNN+SAGBPSO+CSA was approximately near to 98.07%.

Conclusions: This research used SAGBPSO for feature selection and CSA for finalizing the weights and biases of BPNN. The research implemented BPNN, BPNN+SAGBPSO and BPNN+SAGBPSO+CSA on our real time dataset. The proposed hybrid model BPNN+SAGBPSO+CSA outperformed all the state of art deep learning algorithms in terms of performance metrics.

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Author Biographies

Anindita Khade, 1) Department of Computer Engineering, Ramrao Adik Institute of Technology, DY Patil University, Nerul, Maharashtra, India 2) Department of Computer Engineering, SIES Graduate School of Technology, Nerul, Navi Mumbai Maharashtra, India

Anindita Khade is a student of PhD in Computer Engineering in RAIT under DYPU university. She is doing her research work under the guidance of Dr. Vidhate. She is also working as an Assistant Professor in SIES Graduate School of Technology, Nerul. She has over 13 years of teaching experience. She has over 25 research papers in international conferences and journals. Her areas of interest are data analytics, machine learning, artificial intelligence. She can be contacted at aninditaac1987@gmail.com.

Amarsinh V. Vidhate, Department of Computer Engineering, Ramrao Adik Institute of Technology, DY Patil University, Nerul, Maharashtra, India

Amarsinh V. Vidhate is working as a professor & Head, Department of computer engineering at Ramrao Adik Institute of Technology, D Y Patil deemed to be a university, Navi Mumbai, India. He has 26+ years of academic experience and almost 80+ national and international research papers, published at international conferences and referred journals. His areas of research are Protocol Stacks, Computer Networking & Security, VaNET, IoT, and healthcare applications with the assistance of AI, ML & Data Science. He is a PG guide and Ph.D. guide at the University of Mumbai and D Y Patil, Deemed to be University. His special interest is in mass education and designing content that is useful for the masses to make them employable. He is a member of professional bodies like IEEE, CSI, and ISTE. He can be contacted at amar.vidhate@rait.ac.in.

Deepali Vidhate, Department of Biochemistry, School of Medicine DYPU, Nerul, Maharashtra, India

Deepali Vidhate is working as a Professor and Head of the Biochemistry Department at D Y Patil deemed to be University School of Medicine, Navi Mumbai, India. She has 25+ years of academic experience and almost 30+ national and international research papers, published at international conferences and referred journals. Her areas of research are Biomarkers for early prediction of diseases and AI, ML & Data Science applications in medicine. She is a PG guide and Ph.D. guide at the D Y Patil, Deemed to be University. She can be contacted at deepali.vidhate@dypatil.edu.

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Published

2023-10-14

How to Cite

Khade, A. ., Vidhate, A. V. ., & Vidhate, D. . (2023). Design of an Optimized Self-Acclimation Graded Boolean PSO with Back Propagation Model and Cuckoo Search Heuristics for Automatic Prediction of Chronic Kidney Disease. Journal of Mobile Multimedia, 19(06), 1395–1414. https://doi.org/10.13052/jmm1550-4646.1962

Issue

2023: Vol 19 Iss 6

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Articles