A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine Learning

Abstract

Efficient and effective medical diagnostic systems are needed for Autism Spectrum Disorder (ASD) detection and treatment. Healthcare specialists generates extensive remarks on patient behavioural assessment, which is time-consuming to process and record. Early detection of ASD means quality life with the help of appropriate treatment and care. Machine learning models can be utilized to investigate the feasibility of identifying the stated features and evaluating the presence or absence of autism. This study develops a recommender model with multi-classifiers to enhance precision in the prediction of ASD. Various machine learning algorithms are experimented to assess the model’s performance. We show that Decision Trees and Random Forests exhibit improved performance if analyzed with other algorithms with respect to accuracy, precision, recall, and F1-score as evaluation metrics.

Keywords

• Machine learning
• Autism spectrum disorder
• Recommender system
• Multi-classifier approach
• Artificial Neural Network
• Personalized recommendations