Experimental Analysis of Different Autism Detection Models in Machine Learning

Autism, frequently called autism spectrum disorder (ASD), is considered a chief developmental illness impacting people's ability to talk and socialize. It contains a huge variety of troubles marked by difficulties with communication skills, repeated behaviors, speech, and nonverbal communication. The goal of this study was to develop a low-cost, quick, and simple autism detector. Based on a brief, structured questionnaire, the model provided in this investigation was trained to diagnose autism. The questionnaire consists of ten yes or no questions, each of which was connected to the everyday life of an autistic patient. The model identifies whether the user had ASD based on the responses provided by the users. Over the dataset “autism screening adult dataset”, machine learning algorithms such as Naive Bayes (NB), Classification and Regression Tree (CART), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) classifier were used. The most suitable models were found SVM and LDA with an accuracy percentage of 87.07%.