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Feature Signature Discovery for Autism Detection: An Automated Machine Learning Based Feature Ranking Framework
Shomona Gracia Jacob1, Majdi Mohammed Bait Ali Sulaiman2, Bensujin Bennet1
1University of Technology and Applied Sciences, Nizwa, Postal Code: 611, Oman.
Insights
This study introduces an automated machine learning framework for early autism spectrum disorder detection using Q-chat scores. The method achieves high accuracy and identifies significant nonclinical markers across all age groups.
Area of Science:
- Neuroscience and Developmental Psychology
- Artificial Intelligence and Machine Learning
Background:
- Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with varied presentations, impacting social interaction, communication, and behavior.
- Early detection of ASD is crucial for enhancing adaptive skills and independence, yet its exact causes remain elusive and diagnostic methods are evolving.
- Current research indicates a genetic component to ASD, but definitive biomarkers and widely applicable early detection tools are still under development.
Purpose of the Study:
- To apply automated machine learning (AutoML) with feature ranking for identifying significant nonclinical markers for early autism spectrum disorder detection.
- To develop and evaluate a novel AutoML framework that optimizes hyperparameters for ranking potential nonclinical autism markers.
- To report feature signatures and their importance in distinguishing between individuals with and without autism across diverse age groups.
Main Methods:
- Utilized publicly available datasets comprising Q-chat scores from toddlers, children, adolescents, and adults.
- Implemented an automated machine learning framework with hyperparameter optimization for feature ranking.
- Ranked AutoML models using Mathew's correlation coefficient (MCC) and balanced accuracy to identify significant nonclinical markers.
Main Results:
- The proposed AutoML framework achieved approximately 90% MCC and 95% balanced accuracy across all tested age groups.
- Identified and reported significant feature signatures for autism detection, a novel contribution to the field.
- Demonstrated superior performance and flexibility compared to deep learning approaches in marker extraction and adaptability.
Conclusions:
- Automated machine learning offers a flexible and effective approach for early autism spectrum disorder detection by identifying key nonclinical markers.
- The developed framework provides high accuracy and interpretability, facilitating early diagnosis and intervention planning.
- This research highlights the potential of AutoML in advancing diagnostic tools for neurodevelopmental conditions with minimal user intervention.
Abstract:
Autism spectrum disorder is the most used umbrella term for a myriad of neuro-degenerative/developmental conditions typified by inappropriate social behavior, lack of communication/comprehension skills, and restricted mental and emotional maturity. The intriguing factor of this disorder is attributed to the fact that it can be detected only by close monitoring of developmental milestones after childbirth. Moreover, the exact causes for the occurrence of this neurodevelopmental condition are still unknown. Besides, autism is prevalent across individuals irrespective of ethnicity, genetic/familial history, and economic/educational background. Although research suggests that autism is genetic in nature and early detection of this disorder can greatly enhance the independent lifestyle and societal adaptability of affected individuals, there is still a great dearth of information to support the statement of proven facts and figures. This research work places emphasis on the application of automated machine learning incorporated with feature ranking techniques to generate significant feature signatures for the early detection of autism. Publicly available datasets based on the Q-chat scores of individuals across diverse age groups-toddlers, children, adolescents, and adults have been employed in this study. A machine learning framework based on automated hyperparameter optimization is proposed in this work to rank the potential nonclinical markers for autism. Moreover, this study aimed at ranking the AutoML models based on Mathew's correlation coefficient and balanced accuracy via which nonclinical markers were identified from these datasets. Besides, the feature signatures and their significance in distinguishing between classes are being reported for the first time in autism detection. The proposed framework yielded ∼90% MCC and ∼95% balanced accuracy across all four age groups of autism datasets. Deep learning approaches have yielded a maximum of 92.7% accuracy on the same datasets but are limited in their ability to extract significant markers, have not reported on MCC for unbalanced data, and cannot adapt automatically to new data entries. However, AutoML approaches are more flexible, easier to implement, and provide automated optimization, thereby yielding the highest accuracy with minimal user intervention.
