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Comparison of Classification Algorithms of the Autism Spectrum Disorder Diagnosis
Lawi A.
Proceedings 2nd East Indonesia Conference on Computer and Information Technology Internet of Things for Industry Eiconcit 2018
Abstract
ASD sufferers face difficulties in early development compared to normal humans. Various tools, clinical, and non-clinical approaches have been implemented but take a long time to produce a complete diagnosis. the solution by adopting machine learning. This study proposes the application of cross-validation techniques in the Decision Tree method, Linear Discriminant Analysis, Logistic Regression, SVM, and KNN and determines the best k value in each classification method because the shift of datasets when using cross-validation techniques in the classification method is one factor that can cause the estimate to be inaccurate. The results show that the decision tree provides an accuracy of 100% in each of the k values that have been determined previously. 96.9% on Linear Discriminant Analysis with $k=7, k=9$, and $k =10$. 99.7% in Logistic Regression with values of $k=2$ and $k= 3$. 99.9% in Support Vector Machine with values of $k=9$ and $k =1\theta$ and 94.2% for K-Nearest Neighbors with a value of $k=8$.