Comparative Study of Predicating the Learning Disability in children using Machine Learning and Data Mining Techniques

  • Ms. Yogita S. Alone , Dr. G. R. Bamnote

Abstract

A learning setback is not an issue with intellect or motivation Children with learning handicaps are not pitiful or stupid. The goal of the research work is to develop a machine-based learning method to predict people's accurate study impairment, and to calculate effectively in accordance with information obtained from the clinical data, the percentage of learning disability present in children. Learning disorder is a description of a child who has normal learning disabilities, usually due to an unknown factor or factors. The unknown factor is the condition which affects the ability of the brain to receive and process data. The level of intelligence is not indicative of learning disorder. Teachers and parents will be involved in the intervention in order to enable the individual to complete various tasks successfully. There are no indications or profile that can be used for proof of a issue because of the large variations. Some warning signs are nevertheless more common than the different age groups. When everyone is aware about what they are, they will easily and early find a learning disability. n this, we seek to predict childhood learning disorder with the help of different machine learning algorithms

Published
2020-06-01
How to Cite
Ms. Yogita S. Alone , Dr. G. R. Bamnote. (2020). Comparative Study of Predicating the Learning Disability in children using Machine Learning and Data Mining Techniques. International Journal of Advanced Science and Technology, 29(7s), 5341-5349. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/26356