Aim:

          To apply machine learning techniques result in improving the accuracy in the prediction of Autism Spectrum Disorder

Abstract:

       Autism Spectrum Disorder (ASD) is a type of neuro developmental disorder that affects the everyday life of affected patients. Though it is considered hard to completely eradicate this disease, disease severity can be mitigated by taking early interventions. In this paper, we propose an effective framework for the evaluation of various Machine Learning (ML) techniques for the early detection of ASD. The proposed framework employs four different Feature Scaling (FS) strategies i.e., Quantile Transformer (QT), Power Transformer (PT), Normalizer, and Max Abs Scaler (MAS). Then, the feature-scaled datasets are classified through eight simple but effective ML algorithms like Ada Boost (AB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). Our experiments are performed on four standard ASD datasets (Toddlers, Adolescents, Children, and Adults).

Introduction:

       Autism Spectrum Disorder (ASD) is a neuro developmental condition associated with brain development that starts early stage of life, impacting a person’s social relationships and interaction issues. ASD has restricted and repeated behavioral patterns, and the word spectrum encompasses a wide range of symptoms and intensity. Even though there is no sustainable solution for ASD, simply early intervention and proper medical care will make a significant difference in a kid’s development to focus on improving a mchild’s behaviors and skills in communication. Even so, the identification and diagnosis of ASD are really difficult and sophisticated, using traditional behavioral science. Usually, Autism is most commonly diagnosed at about two years of age and can also be diagnosed later, based on its severity. A variety of treatment strategies are available to detect ASD as quickly as possible

Proposed System:

       This research aims to create an effective prediction model using different types of ML methods to detect autism in people of different ages. First of all, the datasets are collected, and then the preprocessing is accomplished via the missing values imputation, Label encoding, and oversampling and Create an instance of RFE with the classifier and the desired number of features to select Logistic Regression classification of modeling, performance evaluation, and the results with improved accuracy.