Abstract
Over 250 million children in developing countries are at risk of not achieving their developmental potential, and unlikely to receive timely interventions because existing developmental assessments that help identify children who are faltering are prohibitive for use in low resource contexts. To bridge this “detection gap,” we developed a tablet-based, gamified cognitive assessment tool named DEvelopmental assessment on an E-Platform (DEEP), which is feasible for delivery by non-specialists in rural Indian households and acceptable to all end-users. Here we provide proof-of-concept of using a supervised machine learning (ML) approach benchmarked to the Bayley’s Scale of Infant and Toddler Development, 3rd Edition (BSID-III) cognitive scale, to predict a child’s cognitive development using metrics derived from gameplay on DEEP. Two-hundred children aged 34–40 months recruited from rural Haryana, India were concurrently assessed using DEEP and BSID-III. Seventy percent of the sample was used for training the ML algorithms using a 10-fold cross validation approach and ensemble modeling, while 30% was assigned to the “test” dataset to evaluate the algorithm’s accuracy on novel data. Of the 522 features that computationally described children’s performance on DEEP, 31 features which together represented all nine games of DEEP were selected in the final model. The predicted DEEP scores were in good agreement (ICC [2,1] > 0.6) and positively correlated (Pearson’s r = 0.67) with BSID-cognitive scores, and model performance metrics were highly comparable between the training and test datasets. Importantly, the mean absolute prediction error was less than three points (<10% error) on a possible range of 31 points on the BSID-cognitive scale in both the training and test datasets. Leveraging the power of ML which allows iterative improvements as more diverse data become available for training, DEEP, pending further validation, holds promise to serve as an acceptable and feasible cognitive assessment tool to bridge the detection gap and support optimum child development.
Highlights
Nurturing care during early childhood leads to lasting positive impacts, including more grades completed in school, and higher adult incomes (Alderman et al, 2017; Nandi et al, 2017; Trevarthen et al, 2018), thereby forming the foundations to achieving the Sustainable Development Goals (Daelmans et al, 2017)
We recently reported the development and piloting of a gamified cognitive assessment tool named DEvelopmental assessment on an E-Platform (DEEP) (Bhavnani et al, 2019), and demonstrated it to be feasible for delivery by non-specialists in rural Indian households and acceptable to children and their families
In this study we explored the potential of using a supervised machine learning (ML) approach benchmarked to the “gold standard” BSID-III cognitive score, to predict a child’s cognitive development using the backend metrics of DEEP
Summary
Nurturing care during early childhood leads to lasting positive impacts, including more grades completed in school, and higher adult incomes (Alderman et al, 2017; Nandi et al, 2017; Trevarthen et al, 2018), thereby forming the foundations to achieving the Sustainable Development Goals (Daelmans et al, 2017). Using proxy measures of poverty and stunting which are known to reflect poor brain development, recent estimates indicate that nearly 250 million children in low and middle-income countries (LMICs) below 5 years of age, of which 65 million live in India, fail to attain their full developmental potential (Lu et al, 2016). Using a more direct measure – the Early Child Development Index (ECDI) – one study suggested that 81 million children in the age group of 3– 4 years alone were developing sub-optimally across 35 LMICs, with sub-Saharan Africa and South Asia contributing the largest numbers (McCoy et al, 2016) While these statistics are alarming, a growing body of evidence suggests that early interventions targeted to optimize development can mitigate the impact of adversities, increase resilience, and protect developmental trajectories (Jeong et al, 2018). This confluence of adverse environments and expensive, resource intensive developmental assessments leads to large “detection” gaps, whereby children with developmental impairments remain unidentified and underserved (Dasgupta et al, 2016)
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