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An early detection of autism spectrum disorder using machine learning
1Department of Computer Science and Engineering, Integral University, Lucknow, India.
Insights
This study uses electroencephalography (EEG) and machine learning to detect autism spectrum disorder (ASD) in newborns. The developed model achieved high accuracy, enabling early ASD identification in infants.
Area of Science:
- Neuroscience
- Machine Learning
- Developmental Pediatrics
Background:
- Autism spectrum disorder (ASD) is a developmental condition affecting communication and social interaction.
- Electroencephalography (EEG) measures brain electrical activity and aids in diagnosing neurological disorders.
- Early ASD detection is challenging due to subtle behavioral indicators in infants under 18 months.
Purpose of the Study:
- To develop a machine learning model for early autism spectrum disorder (ASD) detection in newborns using EEG data.
- To differentiate between infants with ASD and typically developing infants based on EEG patterns.
Main Methods:
- Extracted power values from baseline infant EEG recordings.
- Utilized machine learning classifiers including Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM).
- Employed Explainable AI (SHAP) to interpret model predictions and enhance understanding of the diagnostic rationale.
Main Results:
- The SVM classifier achieved an Area Under the Curve (AUC) of 93%, and the RF classifier achieved 90% AUC.
- Demonstrated exceptional performance in diagnosing infants with ASD.
- Highlighted the novelty of using explainable AI for early ASD identification in infants under one year old.
Conclusions:
- The developed machine learning model can automatically diagnose ASD using infant EEG data.
- This approach facilitates early ASD identification when behavioral signs are not yet apparent.
- EEG analysis combined with AI offers a promising tool for early intervention in autism spectrum disorder.
Background:
Children with autism spectrum disorder (ASD), a developmental disease, exhibit limited and repetitive activities in addition to challenges with communication and social interaction. Numerous neurological disorders have been identified with the aid of the electroencephalography (EEG) technology, which measures the electrical activity of the brain.
Objective:
The primary purpose of this study is to use EEG data to detect ASD in newborns.
Methods:
Power values from baseline EEG recordings of babies are processed and analyzed to extract relevant information. Using machine learning approaches like decision tree (DT), random forest (RF), and support vector machine (SVM) and Explainable AI(SHAP), the model is trained using extracted data to differentiate between those with ASD and those who are usually developing. SHAP (SHapley Additive exPlanations) is a technique that can be used to describe the output of machine learning models. Understanding the rationale behind the predictions produced by the best-performing model is made easier with SHAP.
Results:
Our machine learning model with SVM classifier (AUC = 93 %) and RF classifier (AUC = 90 %) has demonstrated exceptional performance in diagnosing infants with ASD. Novelty: Previously little-focused, this work provides a machine learning model with the use of explainable AI to identify autism spectrum disorder in children under the age of one. Early identification of ASD is challenging since children under the age of 18 months do not exhibit many behavioral indicators. Therefore, medical reports of infants' EEGs are useful in determining whether or not an infant has ASD.
Applications:
As a result, this model may be used to automatically diagnose ASD using the infant's EEG data.
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