Open AccessComparison of Classification Methods used in Machine Learning for Dysgraphia Identification
Author(s) -
Sarthika Dutt
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i11.6142
Subject(s) - dysgraphia , artificial intelligence , identification (biology) , random forest , support vector machine , computer science , machine learning , decision tree , naive bayes classifier , pattern recognition (psychology) , dyslexia , botany , reading (process) , political science , law , biology
Dysgraphia is a disorder that affects writing skills. Dysgraphia Identification at an early age of a child's development is a difficult task. It can be identified using problematic skills associated with Dysgraphia difficulty. In this study motor ability, space knowledge, copying skill, Visual Spatial Response are some of the features included for Dysgraphia identification. The features that affect Dysgraphia disability are analyzed using a feature selection technique EN (Elastic Net). The significant features are classified using machine learning techniques. The classification models compared are KNN (K-Nearest Neighbors), Naïve Bayes, Decision tree, Random Forest, SVM (Support Vector Machine) on the Dysgraphia dataset. Results indicate the highest performance of the Random forest classification model for Dysgraphia identification.