Predictive Modelling of Linear Growth Faltering Among Pediatric Patients with Diarrhea in Rural Western Kenya: An Explainable Machine Learning Approach

Abstract

Abstract Introduction: Stunting affects one-fifth of children globally with diarrhea accounting for an estimated 13.5% of stunting. Identifying risk factors for its precursor, linear growth faltering (LGF), is critical to designing interventions. Moreover, developing new predictive models for LGF using more recent data offers opportunity to improve model performance and capture new insights. We employed machine learning (ML) to derive and validate a predictive model for LGF among children enrolled with diarrhea in the Vaccine Impact on Diarrhea in Africa (VIDA) study and the Enterics for Global Heath (EFGH) ― Shigella study in rural western Kenya. Methods We used 7 ML algorithms to retrospectively build prognostic models for the prediction of LGF (≥ 0.5 decrease in height/length for age z-score [HAZ]) among children 6–35 months. We used de-identified data from the VIDA study (n = 1,473) combined with synthetic data (n = 8,894) in model development, which entailed split-sampling and K-fold cross-validation with over-sampling technique, and data from EFGH-Shigella study (n = 655) for temporal validation. Potential predictors included demographic, household-level characteristics, illness history, anthropometric and clinical data chosen using an explainable model agnostic approach. The champion model was determined based on the area under the curve (AUC) metric. Results The prevalence of LGF in the development and temporal validation cohorts was 187 (16.9%) and 147 (22.4%), respectively. The following variables were associated with LGF in decreasing order: age (16.6%), temperature (6.0%), respiratory rate (4.1%), SAM (3.4%), rotavirus vaccination (3.3%), breastfeeding (3.3%), and skin turgor (2.1%). While all models showed good prediction capability, the gradient boosting model achieved the best performance (AUC% [95% Confidence Interval]: 83.5 [81.6–85.4] and 65.6 [60.8–70.4] on the development and temporal validation datasets, respectively). Conclusion Our findings accentuates the enduring relevance of established predictors of LGF whilst demonstrating the practical utility of ML algorithms for rapid identification of at-risk children.