Prediction of Autism Spectrum Disorder using Voting Classifier based Ensemble Techniques

Autism, often referred to as autism spectrum disorder is a neurodevelopmental behavioral disease that includes challenges like social interactions, lack of focus, communication skills, repetitive behaviors, and child impairment. Screening for autism traits can be both costly and time-intensive. With technological advancements in developing countries, early pre-diction of autism can become more feasible. Detection of ASD in medicine can be efficient using fields like machine learning and pattern recognition. So, we developed a system to predict autism spectrum disorder based on the voting and stacking classifiers of ensemble learning technique which involves training of various algorithms like KNN, SVM, XGB, Logistic Regression, Extra Trees, Decision Tree, Random Forest, AdaBoost and Gradient Boosting.