PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES

Autism spectrum disorder (ASD) is an inherited and neurological developmental disorder characterized by poor social interaction and communication weaknesses. In addition to the clinical methods, machine learning methods have been successfully applied to shorten the duration of the diagnosis and to increase the performance of the diagnosis of the ASD disease. Machine learning methods demonstrate high performance in the diagnosis of diseases with the objective algorithms they offer for the analysis of high-dimensional and multimodal biomedical data. Machine learning methods are successful in identifying the behavioral disorders such as OSB that include heterogeneous conditions because they capture the multivariate relationships in the data and therefore can detect subtle differences in data. In this study, analyzes are performed for the fast and accurate diagnosis of the ASD status using support vector machines (SVM), k-nearest neighbors (kNN) and random forest (RF) machine learning methods using ASD adolescent scan data and the performance of these methods are compared. Accuracy rates of 95%, 89%, and 100% are achieved as a result of binary classification with 10-fold cross-validation (CV) using SVM, kNN, and RF methods, respectively. Furthermore, 100% sensitivity and specificity values were obtained from the classification with RF method. With this study, it has been shown that ASD cases can be detected with complete success as a result of classification with RF method using ASD adult screening data