Prediction of Learning Disability Using Machine Learning

Abstract

Abstract: This A learning disability is a neurological disorder. The children predicted with learning disability may find it difficult to spell, read, write, organize things and so on. Learning disabilities are not related to intelligence or motivation. People with learning disabilities have average or above-average intelligence but may need special accommodations and support to learn and succeed. Early identification, assessment, and intervention are critical for managing learning disabilities. With appropriate support and accommodations, people with learning disabilities can achieve their full potential and lead fulfilling lives. Machine Learning algorithms can be useful in predicting learning disabilities because they can analyse large amounts of data quickly and accurately, and they can identify patterns that may not be apparent to human observers. Deep learning models can lead to better and faster predictions and they are capable to work with unstructured data as well. While Machine Learning and Deep Learning algorithms have shown promise in predicting learning disabilities, it’s important to use these tools in a responsible and ethical manner to ensure that individuals’ privacy and autonomy are protected. By leveraging the power of these algorithms, we can help to ensure that children with learning disabilities receive the support they need to reach their full potential. In this study, six models, ANN and CNN, were assessed for their effectiveness in predicting specific learning disabilities. The performance measure used to evaluate the models was accuracy. Among the models, KNN is found to be the most accurate with 90.33% followed by Random Forest with 79.22%, CNN with 77.69% LSTM with 77.57%, ANN with 57.57% and SVM with 57.57%.