Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms
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 El...
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Nature Portfolio
2025-03-01
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| Online Access: | https://doi.org/10.1038/s41598-025-93495-3 |
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| author | Paramasivam Alagumariappan Malathy Sathyamoorthy Rajesh Kumar Dhanaraj K. Kamalanand C. Emmanuel Sarah Allabun Manal Othman Masresha Getahun Ben Othman Soufiene |
| author_facet | Paramasivam Alagumariappan Malathy Sathyamoorthy Rajesh Kumar Dhanaraj K. Kamalanand C. Emmanuel Sarah Allabun Manal Othman Masresha Getahun Ben Othman Soufiene |
| author_sort | Paramasivam Alagumariappan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f6a747ab3f9c46df98a1d3d13d1d973a |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f6a747ab3f9c46df98a1d3d13d1d973a2025-08-20T02:56:19ZengNature PortfolioScientific Reports2045-23222025-03-0115111610.1038/s41598-025-93495-3Optimized hybrid machine learning framework for early diabetes prediction using electrogastrogramsParamasivam Alagumariappan0Malathy Sathyamoorthy1Rajesh Kumar Dhanaraj2K. Kamalanand3C. Emmanuel4Sarah Allabun5Manal Othman6Masresha Getahun7Ben Othman Soufiene8Department of Biomedical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyDepartment of Information Technology, KPR Institute of Engineering and TechnologySymbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University)Department of Instrumentation Engineering, MIT Campus, Anna UniversityAcademics and Research, Gleneagles Global Health CityDepartment of Medical Education, College of Medicine, Princess Nourah bint Abdulrahman UniversityDepartment of Medical Education, College of Medicine, Princess Nourah bint Abdulrahman UniversityDepartment of Computer Science and Information Technology, College of Engineering and Technology, Kebri Dehar UniversityPRINCE Laboratory Research, ISITcom, Hammam Sousse, University of SousseAbstract 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.https://doi.org/10.1038/s41598-025-93495-3Artificial intelligenceDigestive healthDiagnosisElectrogastrogramsExplainable artificial intelligenceType II diabetes |
| spellingShingle | Paramasivam Alagumariappan Malathy Sathyamoorthy Rajesh Kumar Dhanaraj K. Kamalanand C. Emmanuel Sarah Allabun Manal Othman Masresha Getahun Ben Othman Soufiene Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms Scientific Reports Artificial intelligence Digestive health Diagnosis Electrogastrograms Explainable artificial intelligence Type II diabetes |
| title | Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms |
| title_full | Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms |
| title_fullStr | Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms |
| title_full_unstemmed | Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms |
| title_short | Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms |
| title_sort | optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms |
| topic | Artificial intelligence Digestive health Diagnosis Electrogastrograms Explainable artificial intelligence Type II diabetes |
| url | https://doi.org/10.1038/s41598-025-93495-3 |
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