Machine Learning Model Based on Lipidomic Profile Information to Predict Sudden Infant Death Syndrome

Abstract

Sudden infant death syndrome (SIDS) represents the leading cause of death in under one year of age in developing countries. Even in our century, its etiology is not clear, and there is no biomarker that is discriminative enough to predict the risk of suffering from it. Therefore, in this work, taking a public dataset on the lipidomic profile of babies who died from this syndrome compared to a control group, a univariate analysis was performed using the Mann–Whitney U test, with the aim of identifying the characteristics that enable discriminating between both groups. Those characteristics with a p-value less than or equal to 0.05 were taken; once these characteristics were obtained, classification models were implemented (random forests (RF), logistic regression (LR), support vector machine (SVM) and naive Bayes (NB)). We used seventy percent of the data for model training, subjecting it to a cross-validation (k = 5) and later submitting to validation in a blind test with 30% of the remaining data, which allows simulating the scenario in real life—that is, with an unknown population for the model. The model with the best performance was RF, since in the blind test, it obtained an AUC of 0.9, specificity of 1, and sensitivity of 0.8. The proposed model provides the basis for the construction of a SIDS risk prediction computer tool, which will contribute to prevention, and proposes lines of research to deal with this pathology.

Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach

“…The results showed that the ML model performs better than the standard Poisson regression model and the AUROC of the ML model was 0.89. Karen et al [ 56 ] trained an ML-based early warning model for identifying sudden infant death syndrome using the public data set “Lipidomic in sudden infant death syndrome.” The RF algorithm achieved an AUROC of 0.9 and a recall of 0.8. Ye et al [ 5 ] selected a variety of ML algorithms to build an early real-time early warning system (EWS) to predict the death risk of emergency patients and carried out prospective validation.…”