Prediction of learning disorder: A-systematic review

Abstract

Learning Disorder refers to a number of disorder which may influence the understanding or use of verbal or nonverbal information. The most well-known types of learning disorder involve an issue with reading, writing, listening, and speaking. When we talk about learning disorder, most people only focusing on social development plan. Therefore, in this study, a systematic review was performed to identify, assess and aggregate on the prediction methods used for a predict learning disorder. The main objective of this paper is to, identify the most common prediction methods for learning disorder, in terms of accuracy by using the systematic review technique. From the main objective, we can define the research questions such as, which is the most common and the most accurate prediction methods used for learning disorder. In conclusion, the most common prediction methods for learning disorder which is Decision Tree and Support Vector Machine. For accuracy, Decision Tree, Linear Discriminant Analysis and K-Nearest Neighbor methods have the highest prediction accuracy for a learning disorder. From these findings, this paper can guide others to predict learning disorder by using the most common methods to get the best result in term of accuracy.