An effective autism spectrum disorder screening method using machine learning classification techniques

Nowadays, autism spectrum disorder (ASD) is one of the fastest-growing developmental disorders globally. Screening test consumes more time and expensive to detect the autism signs. Due to advances in artificial intelligence and machine learning (ML), Autism can be predicted at the initial stage. Numerous researches have been conducted using different methods, but none of these researches presented any anticipated results about the capability to predict autism traits under different age groups. Therefore, this paper proposes an effectual prediction method based upon ML strategy to develop a mobile application for predicting ASD of any age people. The autism prediction model is developed by five ML classifiers, such as Gaussian Naive Bayes, Decision Tree Classifier, K-Nearest Neighbors (KNN), Multinomial Logistic Regression (MLR), Support Vector Machine (SVM) and also a mobile application is developed using proposed prediction method. The proposed method is analyzed with IAPQ records gathered from certain areas in Erode district, Tamil Nadu, India. From the analysis, the SVM classifier achieves maximum sensitivity of 23.14%, 6.04%, 5.89%, 11.03% than other classifiers, like Gaussian Naive Bayes, Decision Tree Classifier, KNN, and MLR.