Machine learning predictive modelling for identification of predictors of acute respiratory infection and diarrhoea in Uganda’s rural and urban settings

Abstract

Despite the widely known preventive interventions, the dyad of acute respiratory infections (ARI) and diarrhoea remain among the top global causes of mortality in under– 5 years. Studies on child morbidity have enormously applied “traditional” statistical techniques that have limitations in handling high dimension data, which leads to the exclusion of some variables. Machine Learning (ML) models appear to perform better on high dimension data (dataset with the number of features p (usually correlated) larger than the number of observations N). Using Uganda’s 2006–2016 DHS pooled data on children aged 6–59 months, I applied ML techniques to identify rural-urban differentials in the predictors of child’s diarrhoea and ARI. I also used ML to identify other omitted variables in the current child morbidity frameworks. The predictors were grouped into four categories: child characteristics, maternal characteristics, household characteristics and immunisation. I used 90% of the datasets as a training sets (dataset used to fit (train) a prediction model), which were tested or validated (dataset (pseudo new) used for evaluating the performance of the model on a new dataset) on 10% and 30% datasets. The measure of prediction was based on a 10-fold cross-validation (resampling technique). The gradient-boosted machine (ML technique) was the best-selected model for the identification of the predictors of ARI (Accuracy: 100% -rural and 100%-urban) and diarrhoea (Accuracy: 70%-rural and 100%-urban). These factors relate to the household’s structure and composition, which is characterised by poor hygiene and sanitation and poor household environments that make children more suspectable of developing these diseases; maternal socio-economic factors such as education, occupation, and fertility (birth order); individual risk factors such as child age, birth weight and nutritional status; and protective interventions (immunisation). The study findings confirm the notion that ARI and diarrhoea risk factors overlap. The results highlight the need for a holistic approach with multisectoral emphasis in addressing the occurrence of ARI and diarrhoea among children. In particular, the results provide an insight into the importance of implementing interventions that are responsive to the unique structure and composition of the household. Finally, alongside traditional models, machine learning could be applied in generating research hypotheses and providing insight into the selection of key variables that should be considered in the model.

Predicting health insurance uptake in Kenya using Random Forest: An analysis of socio-economic and demographic factors

“…F1 score is the harmonic mean of precision and recall, and provides a single score that balances precision and recall. Accuracy is the proportion of correct predictions made by the model, but in imbalanced datasets, accuracy can be misleading, as the model may achieve high accuracy by simply predicting the majority class all the time [ 38 , 39 ]…”

“…This approach provides insight into the relationship between these factors and the likelihood of health insurance uptake, enhancing the interpretability of the results. The use of predicted probabilities has been done in other studies [ 39 , 41 , 42 ]…”

Adoption of Artificial Intelligence in the Ugandan Health Sector: a review of Literature

“…Ndagire et al [44] Machine Learning Modelling study of the ability to diagnose acute rheumatic fever at different levels of the Ugandan healthcare system. Kananura [45] Machine Learning Machine learning predictive modelling for identification of predictors of acute respiratory infection and diarrhoea in Uganda's rural and urban settings. Cummings et al [46] Machine Learning Multidimensional analysis of the host response reveals prognostic and pathogen-driven immune subtypes among adults with sepsis in Uganda.…”

“…Very few of the reviewed articles even had ethical clearances to be conducted; this not only violates the research ethical guidelines but is also a danger to data privacy. Some authors described the existing ethical guidelines and policies for implementing healthcare and medical AI-powered systems as a challenge and an issue that requires much attention to address [30,45,50]. Globally, the available laws and policies have been out-paced by rapid technological advancements, and there is a need to revise them to ensure liability [56].…”

The Relationship of Supplementary Feeding, Breast Milk (MP-ASI) to Infants with The Event of Diarrhea

“…Factors that influence diarrhea are risk factors for mothers and toddlers (Demissie et al, 2021;Takele et al, 2019). Maternal factors include age, level of knowledge, education level, employment status, and economic status, while toddler factors (children) include age, exclusive breastfeeding, measles immunization, and nutritional status (Kananura, 2022;Kelkay et al, 2020).…”