Performance Comparison of Dysgraphia Detection Algorithms

This research mainly aims on the comparative analysis of existing popular machine learning algorithms for Dysgraphia detection such as Naive Bayes, KNN (K-Nearest Neighbours), SVM (Support Vector Machine), Decision tree, Random Forest. The evaluation of these algorithms is done on four measure performance measure namely accuracy, precision, F1 score and recall. The comparative statics obtained from the present study depict that although these algorithms are capable to detect dysgraphia upto some extent still there arises a need to develop more effective algorithms for the same at early stages.