Predicting Child Development Status: Can Machine Learning Help?

Machine Learning (ML) has recently been used to solve different problems including detection of child development status, meeting children’s individual learning needs, detecting and intervening developmental problems early, improving rehabilitation processes, and so on. Hence, in this study, we build and use ML models to predict the development status of children based on a recently shared dataset that includes various attributes. To improve the performance of our models, we applied simple and efficient imputing, feature extension, and enlarging techniques on the dataset. The results show that four of ten ML algorithms reach the perfect performance (i.e., an f1-score of 1.0) and this indicates that ML is capable of solving this problem in an automatic and highly accurate way. We therefore believe that the findings of this study can uncover the applicability of ML to solve problems within the related field.