Journal of Computer Technology & Applications

Journal of Computer Technology & Applications Original Research

Autism Spectrum Disorder Prediction Using Classification Techniques: A Comparative Analysis

  1. S. Srividhya Department of Computer Science, KPR College of Arts Science and Research, Coimbatore
  2. Lavanya S.R. Department of Computer Science, KPR College of Arts Science and Research, Coimbatore

Abstract

Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder marked by difficulties in social interaction, communication, and repetitive behaviors. Identifying and addressing ASD early is essential for enhancing the quality of life for those affected. Data mining techniques have emerged as powerful tools in analyzing large datasets to predict and diagnose ASD, aiding in early identification and intervention. This article presents a comprehensive comparative analysis of classification techniques employed in data mining for predicting ASD. ASD, characterized by diverse symptoms and complexities in diagnosis, necessitates advanced methodologies for early detection and intervention. The effectiveness of data mining techniques, such as decision trees, support vector machines, and k-nearest neighbors, is examined for predicting ASD. The comparative analysis focuses on accuracy, precision, and recall to evaluate the strengths and limitations of each technique. Findings from this study aim to provide insights into the applicability of classification methods in ASD prediction, guiding the development of robust models for early identification and intervention strategies. The article emphasizes future directions and challenges, aiming to improve the accuracy and practical implementation of ASD prediction models using data mining techniques.

Keywords

  • Autism spectrum disorder (ASD)
  • K-nearest neighbor
  • classification techniques
  • decision tree
  • support vector machine

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