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Related Experiment Video

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Author Spotlight: Exploring Autism Spectrum Disorder Symptoms in Fruit Flies — Genetic Models and Behavioral Tests

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Reliable Autism Spectrum Disorder Diagnosis for Pediatrics Using Machine Learning and Explainable AI.

Insu Jeon¹, Minjoong Kim², Dayeong So²

¹Department of Medical Science, Soonchunhyang University, Asan 31538, Republic of Korea.

Diagnostics (Basel, Switzerland)

|November 27, 2024

Summary

Machine learning and explainable AI improve autism diagnosis accuracy and transparency. This approach enhances early intervention strategies and clinical trust in AI tools for better patient outcomes.

Keywords:

autism spectrum disorder clinical diagnosis data preprocessing explainable artificial intelligence healthcare analytics machine learning patient outcomes personalized intervention

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Published on: October 4, 2015

Area of Science:

Artificial Intelligence
Machine Learning
Neuroscience

Background:

Increasing demand for early and accurate autism spectrum disorder (ASD) diagnosis.
Emerging role of machine learning (ML) and explainable artificial intelligence (XAI) in improving diagnostic accuracy and transparency.
Potential of AI to revolutionize ASD intervention strategies.

Purpose of the Study:

To present a method combining XAI with data preprocessing for accurate and interpretable ML-based ASD diagnostic tools.
To enhance the transparency of ML models for clinical applications.
To improve clinician trust in AI-driven diagnostic tools.

Main Methods:

Rigorous data preprocessing: outlier removal, missing data handling, feature selection.
Development and comparison of ML algorithms using R and caret package.
Validation via 10-fold cross-validation and hyperparameter tuning with grid search.
Application of XAI techniques for model interpretability.

Main Results:

Data preprocessing enhanced model generalizability and applicability across diverse datasets.
Neural networks and extreme gradient boosting models showed superior performance (accuracy, precision, recall).
XAI revealed significant influence of behavioral features on predictions, increasing interpretability.
Enhanced clinician trust through transparent AI insights.

Conclusions:

Successfully developed precise and interpretable ML models for ASD diagnosis.
Bridged advanced ML methods with clinical practice for AI adoption.
Findings support personalized interventions and early diagnostic practices for improved ASD outcomes.
Facilitated better quality of life for individuals with ASD through AI-driven tools.