{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T11:00:50Z","timestamp":1788865250842,"version":"build-2803163510"},"reference-count":63,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T00:00:00Z","timestamp":1738886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/bioengineering12020160","type":"journal-article","created":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T04:00:28Z","timestamp":1738900828000},"page":"160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Autism Data Classification Using AI Algorithms with Rules: Focused Review"],"prefix":"10.3390","volume":"12","author":[{"given":"Abdulhamid","family":"Alsbakhi","sequence":"first","affiliation":[{"name":"School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fadi","family":"Thabtah","sequence":"additional","affiliation":[{"name":"Abu Dhabi School of Management, Abu Dhabi P.O. Box 6844, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joan","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Abdelhamid, N., Thind, R., Mohammad, H., and Thabtah, F. (2023). Assessing Autistic Traits in Toddlers Using a Data-Driven Approach with DSM-5 Mapping. Bioengineering, 10.","DOI":"10.3390\/bioengineering10101131"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e17","DOI":"10.1542\/peds.2009-0958","article-title":"Randomized, controlled trial of an intervention for toddlers with autism: The Early Start Denver Model","volume":"125","author":"Dawson","year":"2010","journal-title":"Pediatrics"},{"key":"ref_3","unstructured":"Centers for Disease Control and Prevention (2024, October 13). Screening and Diagnosis of Autism Spectrum Disorder, Available online: https:\/\/www.cdc.gov\/autism\/diagnosis\/?CDC_AAref_Val=https:\/\/www.cdc.gov\/ncbddd\/autism\/screening.html."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alshammari, N.K., Alhusaini, A.A., Pasha, A., Ahamed, S.S., Gadekallu, T.R., Abdullah-Al-Wadud, M., Ramadan, R.A., and Alrashidi, M.H. (2024). Explainable Federated Learning for Enhanced Privacy in Autism Prediction Using Deep Learning. J. Disabil. Res., 3.","DOI":"10.57197\/JDR-2024-0081"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, J., Chen, C., Xu, R., and Liu, L. (2024). Autism Identification Based on the Intelligent Analysis of Facial Behaviors: An Approach Combining Coarse-and Fine-Grained Analysis. Children, 11.","DOI":"10.3390\/children11111306"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Duda, M., Ma, R., Haber, N., and Wall, D.P. (2016). Use of Machine Learning for Behavioral Distinction of Autism and ADHD. Transl. Psychiatry, 6.","DOI":"10.1038\/tp.2015.221"},{"key":"ref_7","first-page":"589","article-title":"Autism detection for toddlers from behavioural indicators using classification techniques","volume":"16","author":"Chan","year":"2022","journal-title":"Intell. Decis. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"897","DOI":"10.3233\/THC-202538","article-title":"Improving screening systems of autism using data sampling","volume":"29","author":"Scott","year":"2021","journal-title":"Technol. Health Care"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Abdelhamid, N., Padmavathy, A., Peebles, D., Thabtah, F., and Goulder-Horobin, D. (2020). Data imbalance in autism pre-diagnosis classification systems: An experimental study. J. Inf. Knowl. Manag., 19.","DOI":"10.1142\/S0219649220400146"},{"key":"ref_10","first-page":"1161","article-title":"Neuropsychological features evaluation of data related to Alzheimer\u2019s disease progression using feature engineering","volume":"17","author":"Thabtah","year":"2024","journal-title":"Intell. Decis. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3236009","article-title":"A survey of methods for explaining black box models","volume":"51","author":"Guidotti","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_13","first-page":"1","article-title":"A Machine Learning Framework for Classifying Autism Spectrum Disorder","volume":"44","author":"Thabtah","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_14","unstructured":"Rahman, M.M., Al-Mamun, M.A., Islam, F., and Sarkar, A. (2021). A review of machine learning methods of feature selection and classification for autism spectrum disorder. Artif. Intell. Med., 118."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kavitha, V., and Siva, R. (2022, January 13\u201315). Review of Machine Learning Algorithms for Autism Spectrum Disorder Prediction. Proceedings of the 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India.","DOI":"10.1109\/ICACRS55517.2022.10029201"},{"key":"ref_16","first-page":"113411","article-title":"Machine learning for autism spectrum disorder classification: A comprehensive review","volume":"157","author":"Micheletti","year":"2020","journal-title":"Expert. Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/s41572-019-0138-4","article-title":"Autism spectrum disorder","volume":"6","author":"Lord","year":"2020","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_18","first-page":"1","article-title":"A Survey of Rule Induction Methods for Predictive Data Analytics","volume":"51","author":"Shu","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cohen, W.W. (1995). Fast effective rule induction. Machine Learning Proceedings, Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, CA, USA, 9\u201312 July 1995, Morgan Kaufmann.","DOI":"10.1016\/B978-1-55860-377-6.50023-2"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Karre, K., and Ramadevi, Y. (2023, January 28\u201330). Rule Based Classifier for the Detection of Autism in Children. Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023), Hyderabad, India.","DOI":"10.2991\/978-94-6463-252-1_10"},{"key":"ref_21","first-page":"1","article-title":"A Rule Based Expert System for Autism Diagnosis\/Screening: Prototype Development","volume":"10","author":"Adamu","year":"2022","journal-title":"J. Res. Med. Dent. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"100211","DOI":"10.1016\/j.health.2023.100211","article-title":"A Multi-Classifier-Based Recommender System for Early Autism Spectrum Disorder Detection using Machine Learning","volume":"4","author":"Shinde","year":"2023","journal-title":"Healthc. Anal."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1007\/s00426-022-01761-0","article-title":"Learning and generalization of repetition-based rules in autism","volume":"87","author":"Bettoni","year":"2023","journal-title":"Psychol. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_25","unstructured":"Gini, C. (1936). On the Measure of Concentration with Special Reference to Income and Statistics, Colorado College Publication. General Series No. 208."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1007\/s10803-018-3830-4","article-title":"Autism Spectrum Disorder Decision tree Subgroups Predict Adaptive Behavior and Autism Severity Trajectories in Children with ASD","volume":"49","author":"Cohen","year":"2019","journal-title":"J. Autism Dev. Disord."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1628959","DOI":"10.1155\/2021\/1628959","article-title":"A protocol for the diagnosis of autism spectrum disorder structured in machine learning and verbal decision analysis","volume":"2021","author":"Andrade","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ruan, M., Yu, X., Zhang, N., Hu, C., Wang, S., and Li, X. (2023, January 7\u201310). Video-based contrastive learning on decision trees: From action recognition to autism diagnosis. Proceedings of the 14th Conference on ACM Multimedia Systems, Vancouver, BC, Canada.","DOI":"10.1145\/3587819.3590988"},{"key":"ref_30","first-page":"4139","article-title":"Predicting autism spectrum disorder using machine learning technique","volume":"8","author":"Alwidian","year":"2020","journal-title":"Int. J. Recent. Technol. Eng."},{"key":"ref_31","first-page":"389","article-title":"An Effective Model of Autism Spectrum Disorder Using Machine Learning","volume":"11","author":"Asgarnezhad","year":"2023","journal-title":"Indones. J. Electr. Eng. Inform. (IJEEI)"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4853800","DOI":"10.1155\/2023\/4853800","article-title":"[Retracted] Prediction and Analysis of Autism Spectrum Disorder Using Machine Learning Techniques","volume":"2023","author":"Qureshi","year":"2023","journal-title":"J. Healthc. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3793","DOI":"10.1007\/s13369-020-05165-3","article-title":"Machine learning application for predicting autistic traits in toddlers","volume":"46","author":"Rajab","year":"2021","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_34","unstructured":"Sundari, S. (2020). Association Rule based Autism Spectrum Disorder Diagnosis using Apriori Algorithm. Int. J. Grid Distrib. Comput., 13."},{"key":"ref_35","unstructured":"Rajashekar, P.K.M. (2020, January 17\u201318). Constraint Governed Association Rule Mining for Identification of Strong SNPs to Classify Autism Data. Proceedings of the 2020 International Conference on Communication, Computing and Industry 4.0 (C2I4), Bengaluru, India."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s10618-009-0131-8","article-title":"FURIA: An algorithm for unordered fuzzy rule induction","volume":"19","year":"2009","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_37","first-page":"72","article-title":"Classification of autism disorder severity using fuzzy methods based on soft computing","volume":"8","author":"Saberipour","year":"2023","journal-title":"Eng. Manag. Soft Comput."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lu, S.C., Swisher, C.L., Chung, C., Jaffray, D., and Sidey-Gibbons, C. (2023). On the importance of interpretable machine learning predictions to inform clinical decision making in oncology. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1129380"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1109\/TCDS.2021.3073368","article-title":"Uncertainty modeling for multicenter autism spectrum disorder classification using Takagi\u2013Sugeno\u2013Kang fuzzy systems","volume":"14","author":"Hu","year":"2021","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"16","DOI":"10.29207\/resti.v6i1.3599","article-title":"Optimization of the Fuzzy Logic Method for Autism Spectrum Disorder Diagnosis","volume":"6","author":"Wanti","year":"2022","journal-title":"J. RESTI (Rekayasa Sist. Dan Teknol. Inf.)"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5539\/mas.v17n1p28","article-title":"An Intelligent Technique to Predict the Autism Spectrum Disorder Using Big Data Platform","volume":"17","author":"Alwidian","year":"2023","journal-title":"Mod. Appl. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Garbulowski, M., Smolinska, K., Diamanti, K., Pan, G., Maqbool, K., Feuk, L., and Komorowski, J. (2021). Interpretable machine learning reveals dissimilarities between subtypes of autism spectrum disorder. Front. Genet., 12.","DOI":"10.3389\/fgene.2021.618277"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1109\/TCBB.2022.3163140","article-title":"Jointly composite feature learning and autism spectrum disorder classification using deep multi-output takagi-sugeno-kang fuzzy inference systems","volume":"20","author":"Lu","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/imag_a_00214","article-title":"Applications of interpretable deep learning in neuroimaging: A comprehensive review","volume":"2","author":"Munroe","year":"2024","journal-title":"Imaging Neurosci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Omrani, A.R., Lanovaz, M.J., and Moroni, D. (2024, October 13). Towards the Development of Explainable Machine Learning Models to Recognize the Faces of Autistic Children. Available online: https:\/\/osf.io\/preprints\/psyarxiv\/dp8kb_v1.","DOI":"10.31234\/osf.io\/dp8kb"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s40708-023-00217-4","article-title":"Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders","volume":"11","author":"Saponaro","year":"2024","journal-title":"Brain Inform."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.3233\/THC-240644","article-title":"A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG","volume":"32","author":"Radhakrishnan","year":"2024","journal-title":"Technol. Health Care"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Prasad, P.K.C., Khare, Y., Dadi, K., Vinod, P.K., and Surampudi, B.R. (2022, January 18\u201323). Deep learning approach for classification and interpretation of autism spectrum disorder. Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy.","DOI":"10.1109\/IJCNN55064.2022.9892350"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Awaji, B., Senan, E.M., Olayah, F., Alshari, E.A., Alsulami, M., Abosaq, H.A., Alqahtani, J., and Janrao, P. (2023). Hybrid techniques of facial feature image analysis for early detection of autism spectrum disorder based on combined CNN features. Diagnostics, 13.","DOI":"10.3390\/diagnostics13182948"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Al-Muhanna, M.K., Alghamdi, A.A., Alrfaei, B.M., Afzal, M., Al-Subaiee, R., and Haddadi, R. (2024). An Attention-Based Hybrid Optimized Residual Memory Network (AHRML) Method for Autism Spectrum Disorder (ASD) Detection. J. Disabil. Res., 3.","DOI":"10.57197\/JDR-2024-0030"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1007\/s11571-021-09683-0","article-title":"Classification of ASD based on fMRI data with deep learning","volume":"15","author":"Shao","year":"2021","journal-title":"Cogn. Neurodynamics"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ismail, E., Gad, W., and Hashem, M. (2022). HEC-ASD: A hybrid ensemble-based classification model for predicting autism spectrum disorder disease genes. BMC Bioinform., 23.","DOI":"10.1186\/s12859-022-05099-7"},{"key":"ref_53","first-page":"39","article-title":"Behavioral assessment tools for autism spectrum disorder: An update on the state of the art and challenges","volume":"23","author":"Grynszpan","year":"2021","journal-title":"Curr. Psychiatry Rep."},{"key":"ref_54","first-page":"51","article-title":"Effects of autism spectrum disorder insurance mandates on the identification of autism in the United States","volume":"176","author":"Mandell","year":"2022","journal-title":"JAMA Pediatr."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Am. Psychiatr. Assoc., 21, 591\u2013643.","DOI":"10.1176\/appi.books.9780890425596"},{"key":"ref_56","unstructured":"Lord, C., Rutter, M., DiLavore, P.C., and Risi, S. (2002). Autism Diagnostic Observation Schedule (ADOS), Western Psychological Services."},{"key":"ref_57","unstructured":"(2024, October 13). Regulation (EU) 2016\/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of such Data, and Repealing Directive 95\/46\/EC (General Data Protection Regulation) [2016] OJ L 119\/1. Available online: https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=CELEX%3A32016R0679."},{"key":"ref_58","first-page":"21","article-title":"Challenges in the development of rule-based systems for clinical decision support","volume":"44","author":"Ghasemlou","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_59","first-page":"322","article-title":"The impact of data quality on the performance of rule-based classifiers in healthcare applications","volume":"25","author":"Hosseini","year":"2019","journal-title":"Health Inform. J."},{"key":"ref_60","unstructured":"Mumenin, N., Islam, M.F., Chowdhury, M.R.Z., and Yousuf, M.A. (2022, January 29\u201330). Diagnosis of autism spectrum disorder through eye movement tracking using deep learning. Proceedings of the International Conference on Information and Communication Technology for Development: ICICTD 2022, Khulna, Bangladesh."},{"key":"ref_61","first-page":"45","article-title":"Explainable Autism Spectrum Disorder Model (EASDM): A hybrid approach combining SHAP and deep learning","volume":"12","author":"Atlam","year":"2024","journal-title":"J. Data Sci. Artif. Intell. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"6159","DOI":"10.1007\/s40747-024-01432-0","article-title":"Prioritizing complex health levels beyond autism triage using fuzzy multi-criteria decision-making","volume":"10","author":"Albahri","year":"2024","journal-title":"Complex. Intell. Syst."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/BF02172145","article-title":"Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders","volume":"24","author":"Lord","year":"1994","journal-title":"J. Autism Dev. Disord."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/2\/160\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:28:35Z","timestamp":1760027315000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/2\/160"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,7]]},"references-count":63,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["bioengineering12020160"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering12020160","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,7]]}}}