Metabolomics Biomarkers in Prediction of Sudden Infant Death Syndrome: The Role of Short Chain Fatty Acids
Sudden Infant Death Syndrome (SIDS) presents a significant challenge, necessitating ongoing research and preventive measures. The intricate landscape of lipid metabolism plays a crucial role in SIDS, with disruptions in key lipid components like Short Chain Fatty Acids (SCFA), alongside other lipids...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10811927/ |
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| author | Maria Aslam Omer Riaz Jawaria Aslam Dost Muhammad Khan Mustafa Hameed Muhammad Suleman Rizwan Shahid Turke Althobaiti Naeem Ramzan |
| author_facet | Maria Aslam Omer Riaz Jawaria Aslam Dost Muhammad Khan Mustafa Hameed Muhammad Suleman Rizwan Shahid Turke Althobaiti Naeem Ramzan |
| author_sort | Maria Aslam |
| collection | DOAJ |
| description | Sudden Infant Death Syndrome (SIDS) presents a significant challenge, necessitating ongoing research and preventive measures. The intricate landscape of lipid metabolism plays a crucial role in SIDS, with disruptions in key lipid components like Short Chain Fatty Acids (SCFA), alongside other lipids such as triglycerides (TG) and phospholipids (PL), being significant. In this context, SCFA are essential products of the fermentation process by gut microbiota, hold particular interest. SCFA are integral to energy regulation and metabolism, influencing overall well-being. Their unique characteristics, such as chain length and saturation level, provide insights into their potential effects. Alterations in SCFA metabolism can disrupt energy balance, adding to the complexity of SIDS. Leveraging machine learning (ML) presents a promising avenue for unraveling the intricate profiles of SCFA and decoding patterns indicative of heightened SIDS risk. Ensuring interpretability in healthcare is essential for building trust and developing effective prevention strategies. This research delves into understanding SIDS, with a specific focus on SCFA and their role in metabolic health. The application of ML, particularly the Artificial Neural Network (ANN) and Stacking model, demonstrated exceptional accuracy of 94% and 96.15% with a recall of 100% and 92.31%, respectively. The models also demonstrated strong classification capabilities, as indicated by a high True Positive Rate (TPR) in the AUC, a low Root Mean Square Error (RMSE) of 0.20, Mean Absolute Error (MAE) of 0.04 and Standard deviation (SD) of 0.10, emphasizing the robustness and precision of the approach. These results underscore the potential of ML in the early assessment of SIDS risk, highlighting the critical role of SCFA and advancing the prospects for preventative healthcare. |
| format | Article |
| id | doaj-art-0e941dbf9c224b9aaf56210cfba1a824 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0e941dbf9c224b9aaf56210cfba1a8242025-01-28T00:01:31ZengIEEEIEEE Access2169-35362025-01-0113148201483610.1109/ACCESS.2024.352111610811927Metabolomics Biomarkers in Prediction of Sudden Infant Death Syndrome: The Role of Short Chain Fatty AcidsMaria Aslam0https://orcid.org/0000-0002-5376-013XOmer Riaz1Jawaria Aslam2https://orcid.org/0000-0002-7562-3830Dost Muhammad Khan3https://orcid.org/0000-0001-9303-651XMustafa Hameed4https://orcid.org/0000-0003-4354-6073Muhammad Suleman5Rizwan Shahid6Turke Althobaiti7https://orcid.org/0000-0002-6674-7890Naeem Ramzan8https://orcid.org/0000-0002-5088-1462Department of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Biochemistry and Physiology, Cholistan University of Veterinary and Animal Sciences (CUVAS) Bahawalpur, Bahawalpur, Punjab, PakistanDepartment of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Information Technology, The Islamia University of Bahawalpur, Bahawalpur, PakistanUnited Lincolnshire Hospitals NHS Trust, Lincoln County Hospital, Lincoln, U.K.Department of Computer Science, Faculty of Science, Northern Border University, Arar, Saudi ArabiaSchool of Computing Engineering and Physical Sciences, University of the West of Scotland, Paisley, U.K.Sudden Infant Death Syndrome (SIDS) presents a significant challenge, necessitating ongoing research and preventive measures. The intricate landscape of lipid metabolism plays a crucial role in SIDS, with disruptions in key lipid components like Short Chain Fatty Acids (SCFA), alongside other lipids such as triglycerides (TG) and phospholipids (PL), being significant. In this context, SCFA are essential products of the fermentation process by gut microbiota, hold particular interest. SCFA are integral to energy regulation and metabolism, influencing overall well-being. Their unique characteristics, such as chain length and saturation level, provide insights into their potential effects. Alterations in SCFA metabolism can disrupt energy balance, adding to the complexity of SIDS. Leveraging machine learning (ML) presents a promising avenue for unraveling the intricate profiles of SCFA and decoding patterns indicative of heightened SIDS risk. Ensuring interpretability in healthcare is essential for building trust and developing effective prevention strategies. This research delves into understanding SIDS, with a specific focus on SCFA and their role in metabolic health. The application of ML, particularly the Artificial Neural Network (ANN) and Stacking model, demonstrated exceptional accuracy of 94% and 96.15% with a recall of 100% and 92.31%, respectively. The models also demonstrated strong classification capabilities, as indicated by a high True Positive Rate (TPR) in the AUC, a low Root Mean Square Error (RMSE) of 0.20, Mean Absolute Error (MAE) of 0.04 and Standard deviation (SD) of 0.10, emphasizing the robustness and precision of the approach. These results underscore the potential of ML in the early assessment of SIDS risk, highlighting the critical role of SCFA and advancing the prospects for preventative healthcare.https://ieeexplore.ieee.org/document/10811927/Sudden infant death syndromebiomarkersmachine learningfatty acidsshort chain fatty acidshealthcare |
| spellingShingle | Maria Aslam Omer Riaz Jawaria Aslam Dost Muhammad Khan Mustafa Hameed Muhammad Suleman Rizwan Shahid Turke Althobaiti Naeem Ramzan Metabolomics Biomarkers in Prediction of Sudden Infant Death Syndrome: The Role of Short Chain Fatty Acids IEEE Access Sudden infant death syndrome biomarkers machine learning fatty acids short chain fatty acids healthcare |
| title | Metabolomics Biomarkers in Prediction of Sudden Infant Death Syndrome: The Role of Short Chain Fatty Acids |
| title_full | Metabolomics Biomarkers in Prediction of Sudden Infant Death Syndrome: The Role of Short Chain Fatty Acids |
| title_fullStr | Metabolomics Biomarkers in Prediction of Sudden Infant Death Syndrome: The Role of Short Chain Fatty Acids |
| title_full_unstemmed | Metabolomics Biomarkers in Prediction of Sudden Infant Death Syndrome: The Role of Short Chain Fatty Acids |
| title_short | Metabolomics Biomarkers in Prediction of Sudden Infant Death Syndrome: The Role of Short Chain Fatty Acids |
| title_sort | metabolomics biomarkers in prediction of sudden infant death syndrome the role of short chain fatty acids |
| topic | Sudden infant death syndrome biomarkers machine learning fatty acids short chain fatty acids healthcare |
| url | https://ieeexplore.ieee.org/document/10811927/ |
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