doi: 10.18178/ijiet.2024.14.2.2045
Machine Learning Based Developmental Capability Prediction: A Diagnosis to the Learning Capacity Disorder for Specially-Abled Children
Priya Chandran*
,
Suhasini Vijaykumar ,
Gunjan Behl ,
Shravani Pawar ,
Nidhi ,
Manish Dubey ,
Vasudha Arora
- Bharati Vidyapeeth’s Institute of Management & Information Technology, Navi Mumbai, Maharashtra, India
- Manuscript receivedJuly 3, 2023
- revisedAugust 8, 2023
- acceptedNovember 1, 2023
- publishedFebruary 15, 2024
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.
Keywords
- learning disorders
- ASD
- disability
- occupational therapy
- speech therapy
- machine learning
How to Cite
Priya Chandran, Suhasini Vijaykumar, Gunjan Behl, Shravani Pawar, Nidhi, Manish Dubey, and Vasudha Arora, "Machine Learning Based Developmental Capability Prediction: A Diagnosis to the Learning Capacity Disorder for Specially-Abled Children," International Journal of Information and Education Technology, vol. 14, no. 2, pp. 240-247, 2024. https://doi.org/10.18178/ijiet.2024.14.2.2045
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).