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MARIA ASLAM, OMER RIAZ, JAWARIA ASLAM, DOST MUHAMMAD KHAN, MUSTAFA HAMEED, MUHAMMAD SULEMAN, RIZWAN SHAHID, TURKE ALTHOBAITI, AND NAEEM RAMZAN, published a research articles IEEE Access

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.