Accuracy, Recall, Precision of SVM Kernels in Predicting Autistic Spectrum Disorder In Adults
DidikSetiyadi1, Muhammad Dwison Alizah2, Yulius Paulus Dharsono3, SabarSautomo4, Sfenrianto5

1Didik Setiyadi, Department of Informatics, Bina Insani University, Indonesia.
2Muhammad Dwison Alizah*, Departement of Computer Science, STMIK Nusa Mandiri, Jakarta, Indonesia.
3Yulius Paulus Dharsono, Departement of Computer Science, STMIK Nusa Mandiri, Jakarta, Indonesia.
4SabarSautomo, Departement of Computer Science, STMIK Nusa Mandiri, Jakarta, Indonesia.
5Sfenrianto, Information Systems Management Department, BINUS Graduate Program, Master of Information Systems Management, Bina Nusantara University, Jakarta, Indonesia.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 2215-2218 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7655038620/2020©BEIESP | DOI: 10.35940/ijrte.F7655.038620

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Abstract: Autism is a disorder that is quite difficult to diagnose when the condition of the sufferer is in the adult category. In this era, technology has been able to make predictions including health cases. Autistic Spectrum Disorder (ASD) in adults is felt to be predictable by using machine learning. This study will build a predictor for ASD sufferers. Predictors of machine learning are built using the Support Vector Machine (SVM) algorithm, with the type of kernel used was Gaussian RBF, Polynomial and Sigmoid. From the predictors that are built, the best SVM parameters will be searched based on accuracy. This best parameter is used to build the best new predictor and the results of the prediction are compared in terms of accuracy, recall, and precision. These results can be used to get the best performance when detecting ASD sufferers effectively and efficiently.
Keywords: Autism, Machine Learning, SVM, Kernel, Accuracy, Recall, Precision.
Scope of the Article: Machine Learning.