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Alagumariappan P, Sathyamoorthy M, Dhanaraj RK, Kamalanand K, Emmanuel C, Allabun S, Othman M, Getahun M, Soufiene BO. Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms. Sci Rep 2025; 15:8875. [PMID: 40087479 PMCID: PMC11909154 DOI: 10.1038/s41598-025-93495-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2025] [Accepted: 03/07/2025] [Indexed: 03/17/2025] Open
Abstract
In recent years, diabetes has become a global public health problem, and it is reported that the migrant Indians have more prevalence rate of Type-II diabetes. Also, the type-II diabetes in Indians are increased to a large extent due to modern lifestyle, food habits etc. In this work, an ElectroGastroGram (EGG) based non-invasive assessment for early prediction of type-II diabetes is proposed. Furthermore, the EGG signals are acquired from normal individuals and people with an age group between 50 and 65 who are suffering from Type-II diabetes using three electrode EGG acquisition devices. Also, the Explainable Artificial Intelligence (XAI) especially SHapley Additive exPlanations (SHAP) and Meta-Heuristics based feature selection methods are utilized to determine the prominent EGG signal features. A framework is devised using Meta-Heuristic based Hybrid Extreme Gradient (MH-XGB) Boost Classifier for an efficient classification of normal EGG signals and diabetic EGG signals. The proposed MH-XGB classifier is compared with the benchmark models namely Random Forest (RF) classifier and conventional Extreme Gradient Boosting (XGBoost) classifier by using performance metrics. Results demonstrate that the proposed MH-XGB classifier exhibits accuracy, sensitivity, specificity of 95.8%, 100%, and 92.3% respectively which is superior to other benchmark models. Additionally, it is demonstrated that the AUC, F1 Score and False Positive Rate (FPR) of the proposed MH-XGB classifier is 0.9545, 0.96 and 0.077 respectively. The proposed method is highly useful for early prediction of real-time societal disease (diabetes-Type-II) in an effective manner.
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Affiliation(s)
- Paramasivam Alagumariappan
- Department of Biomedical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India
| | - Malathy Sathyamoorthy
- Department of Information Technology, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India
| | - Rajesh Kumar Dhanaraj
- Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India
| | - K Kamalanand
- Department of Instrumentation Engineering, MIT Campus, Anna University, Chennai, India
| | - C Emmanuel
- Academics and Research, Gleneagles Global Health City, Chennai, India
| | - Sarah Allabun
- Department of Medical Education, College of Medicine, Princess Nourah bint Abdulrahman University, P.O.Box 84428, 11671, Riyadh, Saudi Arabia
| | - Manal Othman
- Department of Medical Education, College of Medicine, Princess Nourah bint Abdulrahman University, P.O.Box 84428, 11671, Riyadh, Saudi Arabia
| | - Masresha Getahun
- Department of Computer Science and Information Technology, College of Engineering and Technology, Kebri Dehar University, Kebri Dehar, Ethiopia.
| | - Ben Othman Soufiene
- PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Sousse, Tunisia
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Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals. SENSORS 2021; 21:s21186064. [PMID: 34577270 PMCID: PMC8469046 DOI: 10.3390/s21186064] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 08/31/2021] [Accepted: 09/07/2021] [Indexed: 01/18/2023]
Abstract
Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).
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