Analyzing the Predictive Power of Machine Learning Models for Autism Detection - Preprint study

Aug 3, 2026

This study investigates how machine learning models can support early detection of Autism Spectrum Disorder (ASD), where timely diagnosis and intervention improve outcomes for individuals and families. Multiple algorithms—including Decision Tree, Random Forest, Support Vector Machine, and k-Nearest Neighbors—are compared using F1-Score, accuracy, precision, and recall. Multi-layer Perceptron (MLP) achieves the strongest results with an F1-Score of 79.35%. Feature importance analysis emphasizes influences from gender, genetic predisposition, age at diagnosis, and ethnicity-related factors.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Introduction

Autism and the need for early detection

Study objective and evaluation metrics

Methodology

Dataset and preprocessing steps

Machine learning models compared

Publication status: Not informed by the submitting author Analyzing the Predictive Power of Machine Learning Models for Autism Detection Dheiver Francisco Santos [https://doi.org/10.1590/SciELOPreprints.7184](https://doi.org/10.1590/SciELOPreprints.7184) Submitted on: 2023-10-15 Posted on: 2023-10-27 (version 1)(YYYY-MM-DD) SciELO Preprints-This document is a preprint and its current status is available at: [https://doi.org/10.1590/SciELOPreprints.7184](https://doi.org/10.1590/SciELOPreprints.7184) Analyzing the Predictive Power of Machine Learning Models for Autism Detection Dheiver Francisco Santos CATI-Advanced Center for Intelligent Technologies Av. Álvaro Otacílio, 508-Jatiúca Maceió -AL, 57035-180 Email: [dheiver.santos@gmail.com](dheiver.santos@gmail.com) [Tel.:](Tel.:) +55 51 98988-9898 ORCID: [https://orcid.org/0000-0002-8599-9436](https://orcid.org/0000-0002-8599-9436) Abstract This study delves into the application of machine learning models for the early detection of Autism Spectrum Disorder (ASD) . Early diagnosis and intervention are critical for improving the lives of individuals with ASD and their families. This research compares various machine learning models, including Decision Tree, Random Forest, Support Vector Machine, k-Nearest Neighbors, and more, assessing their performance based on key metrics such as F 1-Score, accuracy, precision, and recall. The study reveals the Multi-layer Perceptron (MLP) as the top-performing model with an impressive F 1-Score of 79.35%, demonstrating its potential for accurate ASD detection. The feature importance analysis highlights the significant roles of gender, genetic predisposition, age at diagnosis, and ethnicity-related features in predicting ASD. This study underscores the promise of machine learning in ASD detection and emphasizes the importance of early intervention and personalized approaches to diagnosis. Keywords: Autism Spectrum Disorder, machine learning, predictive modeling, early diagnosis, F 1-Score, feature importance, Multi-layer Perceptron, ethnicity, gender, genetic predisposition. Introduction: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that affects millions of individuals worldwide. Early diagnosis and intervention are crucial for improving the quality of life and developmental outcomes for those with ASD. Machine learning has emerged as a powerful tool to aid in the early detection of ASD, offering the potential to augment traditional diagnostic methods. SciELO Preprints-This document is a preprint and its current status is available at: [https://doi.org/10.1590/SciELOPreprints.7184](https://doi.org/10.1590/SciELOPreprints.7184) This paper presents a comprehensive analysis of the predictive power of various machine learning models in the context of ASD detection. The study draws inspiration from several pioneering works in the field, as highlighted by Leblanc et al. (2020), Hasan et al. (2022), Thabtah and Peebles (2020), and other researchers who have contributed significantly to the application of machine learning in autism research. The references cited in this study provide critical insights into the importance of reliable and early detection of autism and underscore the urgency of developing effective machine learning models for this purpose. The primary objective of this work is to assess and compare the performance of different machine learning models, including Decision Tree, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors, among others, in their ability to accurately predict autism. We aim to evaluate the models based on metrics such as F 1-Score, accuracy, precision, and recall. By leveraging these models and metrics, we endeavor to identify the most reliable and effective approach to ASD detection using machine learning techniques. Through this analysis, we aspire to shed light on the potential of machine learning in improving the early diagnosis of

FAQs

Which machine learning models were compared for autism detection in this study?

Decision Tree, Random Forest, Support Vector Machine, k-Nearest Neighbors, and additional algorithms such as Gradient Boosting, Gaussian Naive Bayes, Logistic Regression, and Multi-layer Perceptron are included in the comparison.

What performance metrics were used to evaluate the models?

Models were assessed using F1-Score, accuracy, precision, and recall, enabling performance comparison across classification behavior.

Which model performed best and what features were most influential?

The Multi-layer Perceptron (MLP) performed best, reaching an F1-Score of 79.35%. Feature importance highlights gender, genetic predisposition, age at diagnosis, and ethnicity-related attributes as key predictors.

Aug 3, 2026

Upload by Olivia Brown