Use of artificial intelligence on Electroencephalogram (EEG) waveforms to predict failure in early school grades in children from a rural cohort in Pakistan

Universal primary education is critical for individual academic growth and overall adult productivity of nations. Estimates indicate that 25% of 59 million primary age out of school children drop out and early grade failure is one of the factors. An objective and feasible screening measure to identify at-risk children in the early grades can help to design appropriate interventions. The objective of this study was to use a Machine Learning algorithm to evaluate the power of Electroencephalogram (EEG) data collected at age 4 in predicting academic achievement at age 8 among rural children in Pakistan. Demographic and EEG data from 96 children of a cohort along with their academic achievement in grade 1–2 measured using an academic achievement test of Math and language at the age of 7–8 years was used to develop the machine learning algorithm. K- Nearest Neighbor (KNN) classifier was used on different model combinations of EEG, sociodemographic and home environment variables. KNN model was evaluated using 5 Stratified Folds based on the sensitivity and specificity. In the current dataset, 55% and 74% failed in the mathematics and language test respectively. On testing data across each fold, the mean sensitivity and specificity was calculated. Sensitivity was similar when EEG variables were combined with sociodemographic, and home environment (Math = 58.7%, Language = 66.3%) variables but specificity improved (Math = 43.4% to 50.6% and Language = 32% to 60%). The model requires further validation for EEG to be used as a screening measure with adequate sensitivity and specificity to identify children in their preschool age who may be at high risk of failure in early grades.