Machine Learning Based Developmental Capability Prediction: A Diagnosis to the Learning Capacity Disorder for Specially-Abled Children

Abstract

Specially-abled people are recognized and acknowledged for their issues such as hyperactivity, learning disorder, proprioceptive sensory issues, problems in self-help skills and problems in various motor skills such as Gross Motor Skills (GMS), Fine Motor Skills (FMS) and Oral Motor Skills (OMS), This study sought to identify effective machine-learning-based classification models to predict developmental capability disorders and thereby addressing of the learning disorder issue at opportune time. We have used machine learning classification algorithms Decision Tree, Random Forest, K-nearest neighbors, and Logistic Regression for the developmental capability prediction of individuals. The generalized progress monitoring datasets were carried out by interpreting and visualizing gender, age and disability-specific developmental competence. We have collected dataset from an occupational therapist for the study. The results of the study show that the Random Forest algorithm has a high accuracy of 95% compared to other algorithms that we have implemented.