Autism Data Classification Using AI Algorithms with Rules: Focused Review Article Swipe
Abdulhamid Alsbakhi
,
Fadi Thabtah
,
Joan Lu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering12020160
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering12020160
Autism Spectrum Disorder (ASD) presents challenges in early screening due to its varied nature and sophisticated early signs. From a machine-learning (ML) perspective, the primary challenges include the need for large, diverse datasets, managing the variability in ASD symptoms, providing easy-to-understand models, and ensuring ASD predictive models that can be employed across different populations. Interpretable or explainable classification algorithms, like rule-based or decision tree, play a crucial role in dealing with some of these issues by offering classification models that can be exploited by clinicians. These models offer transparency in decision-making, allowing clinicians to understand reasons behind diagnostic decisions, which is critical for trust and adoption in medical settings. In addition, interpretable classification algorithms facilitate the identification of important behavioural features and patterns associated with ASD, enabling more accurate and explainable diagnoses. However, there is a scarcity of review papers focusing on interpretable classifiers for ASD detection from a behavioural perspective. Thereby this research aimed to conduct a recent review on rule-based classification research works in order to provide added value by consolidating current research, identifying gaps, and guiding future studies. Our research would enhance the understanding of these techniques, based on data used to generate models and obtain performance by trying to highlight early detection and intervention ways for ASD. Integrating advanced AI methods like deep learning with rule-based classifiers can improve model interpretability, exploration, and accuracy in ASD-detection applications. While this hybrid approach has feature selection relevant features that can be detected in an efficient manner, rule-based classifiers can provide clinicians with transparent explanations for model decisions. This hybrid approach is critical in clinical applications like ASD, where model content is as crucial as achieving high classification accuracy.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3390/bioengineering12020160
- OA Status
- gold
- Cited By
- 2
- References
- 51
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4407255853Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/bioengineering12020160Digital Object Identifier
- Title
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Autism Data Classification Using AI Algorithms with Rules: Focused ReviewWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-07Full publication date if available
- Authors
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Abdulhamid Alsbakhi, Fadi Thabtah, Joan LuList of authors in order
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https://doi.org/10.3390/bioengineering12020160Publisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/bioengineering12020160Direct OA link when available
- Concepts
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Interpretability, Machine learning, Computer science, Artificial intelligence, Autism spectrum disorder, Decision tree, Identification (biology), Medical diagnosis, Autism, Data science, Psychology, Medicine, Psychiatry, Botany, Biology, PathologyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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| referenced_works | https://openalex.org/W4387097495, https://openalex.org/W2144236040, https://openalex.org/W4402286572, https://openalex.org/W4403835160, https://openalex.org/W2263602591, https://openalex.org/W6838349022, https://openalex.org/W3119907719, https://openalex.org/W3010894680, https://openalex.org/W2945976633, https://openalex.org/W2962772482, https://openalex.org/W3112948313, https://openalex.org/W4319431148, https://openalex.org/W2769544142, https://openalex.org/W1670263352, https://openalex.org/W4388488127, https://openalex.org/W6903675121, https://openalex.org/W4382585365, https://openalex.org/W4308624891, https://openalex.org/W4236137412, https://openalex.org/W1995875735, https://openalex.org/W2902352354, https://openalex.org/W3150759140, https://openalex.org/W4366732996, https://openalex.org/W3119099123, https://openalex.org/W6854219720, https://openalex.org/W4383746175, https://openalex.org/W3121476122, https://openalex.org/W3134794168, https://openalex.org/W2164400088, https://openalex.org/W6980601309, https://openalex.org/W4322752859, https://openalex.org/W3156846092, https://openalex.org/W4210432535, https://openalex.org/W4362576192, https://openalex.org/W3134573349, https://openalex.org/W4221008001, https://openalex.org/W4399662619, https://openalex.org/W4395446121, https://openalex.org/W4390730860, https://openalex.org/W4400815253, https://openalex.org/W4312302217, https://openalex.org/W4386780968, https://openalex.org/W4394895913, https://openalex.org/W3163463488, https://openalex.org/W4312043191, https://openalex.org/W4247665917, https://openalex.org/W4318003502, https://openalex.org/W4399334743, https://openalex.org/W1980309199, https://openalex.org/W4281931456, https://openalex.org/W4382807332 |
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