A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI
<p dir="ltr">Autism Spectrum Disorder (ASD) is a genetic and neurological condition that leads to difficulties in communication and social interaction. The global concern associated with ASD diagnosis is increasing at a rapid rate due to its significant impact on quality of life. Ear...
محفوظ في:
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2025
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إضافة وسم
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| _version_ | 1864513531584446464 |
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| author | Oishi Jyoti (21593819) |
| author2 | Hafsa Binte Kibria (22828085) Zareen Tasnim Pear (22828088) Md Nahiduzzaman (9092546) Md. Faysal Ahamed (21842396) Khandaker Reajul Islam (16904832) Jaya Kumar (13896771) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author author author author author author |
| author_facet | Oishi Jyoti (21593819) Hafsa Binte Kibria (22828085) Zareen Tasnim Pear (22828088) Md Nahiduzzaman (9092546) Md. Faysal Ahamed (21842396) Khandaker Reajul Islam (16904832) Jaya Kumar (13896771) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Oishi Jyoti (21593819) Hafsa Binte Kibria (22828085) Zareen Tasnim Pear (22828088) Md Nahiduzzaman (9092546) Md. Faysal Ahamed (21842396) Khandaker Reajul Islam (16904832) Jaya Kumar (13896771) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2025-07-17T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3586314 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Clinically_Interpretable_Approach_for_Early_Detection_of_Autism_Using_Machine_Learning_With_Explainable_AI/30860177 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning Autism spectrum disorder (ASD) artificial intelligence (AI) machine learning (ML) explainable AI Accuracy Predictive models Autism Explainable AI Support vector machines Random forests Machine learning Machine learning algorithms Diseases Reliability |
| dc.title.none.fl_str_mv | A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Autism Spectrum Disorder (ASD) is a genetic and neurological condition that leads to difficulties in communication and social interaction. The global concern associated with ASD diagnosis is increasing at a rapid rate due to its significant impact on quality of life. Early identification of ASD can significantly support timely intervention and treatment planning. While research in ASD diagnosis is evolving through the application of machine learning (ML) techniques, practical implementation in clinical settings has not progressed at the same pace. Although theoretical studies have demonstrated improved ML performance, they have not gained much interest among clinicians due to a lack of explainability. This study focuses on optimizing and comparing various machine learning models for ASD diagnosis, while incorporating explainable AI techniques to ensure model transparency and interpretability. The paper uses naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) as classifiers after careful investigation. Three different publicly available datasets have been used based on the age group to create the best predicting model for each case. After handling missing values, balancing the dataset, and analyzing the classifier’s performance, it is found that tree-based algorithms, particularly RF, perform better for all the datasets. The RF model achieved up to 99% balanced accuracy on the adult dataset, with similarly strong performance on the children and adolescent datasets using five-fold cross-validation (CV). SHAP are also illustrated to improve model interpretability by highlighting the most influential features, thereby aiding physician understanding. The novelty of this work lies in the integration of explainable AI with robust preprocessing and age-specific modeling across multiple ASD datasets, addressing both diagnostic accuracy and clinical interpretability. It can be considered that the suggested method can efficiently diagnose ASD at a very early stage and enhance the understanding of ASD diagnosis clinically.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3586314" target="_blank">https://dx.doi.org/10.1109/access.2025.3586314</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_dcff52219765dcdfe9adf91cc92251ff |
| identifier_str_mv | 10.1109/access.2025.3586314 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30860177 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AIOishi Jyoti (21593819)Hafsa Binte Kibria (22828085)Zareen Tasnim Pear (22828088)Md Nahiduzzaman (9092546)Md. Faysal Ahamed (21842396)Khandaker Reajul Islam (16904832)Jaya Kumar (13896771)Muhammad E. H. Chowdhury (14150526)EngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningAutism spectrum disorder (ASD)artificial intelligence (AI)machine learning (ML)explainable AIAccuracyPredictive modelsAutismExplainable AISupport vector machinesRandom forestsMachine learningMachine learning algorithmsDiseasesReliability<p dir="ltr">Autism Spectrum Disorder (ASD) is a genetic and neurological condition that leads to difficulties in communication and social interaction. The global concern associated with ASD diagnosis is increasing at a rapid rate due to its significant impact on quality of life. Early identification of ASD can significantly support timely intervention and treatment planning. While research in ASD diagnosis is evolving through the application of machine learning (ML) techniques, practical implementation in clinical settings has not progressed at the same pace. Although theoretical studies have demonstrated improved ML performance, they have not gained much interest among clinicians due to a lack of explainability. This study focuses on optimizing and comparing various machine learning models for ASD diagnosis, while incorporating explainable AI techniques to ensure model transparency and interpretability. The paper uses naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) as classifiers after careful investigation. Three different publicly available datasets have been used based on the age group to create the best predicting model for each case. After handling missing values, balancing the dataset, and analyzing the classifier’s performance, it is found that tree-based algorithms, particularly RF, perform better for all the datasets. The RF model achieved up to 99% balanced accuracy on the adult dataset, with similarly strong performance on the children and adolescent datasets using five-fold cross-validation (CV). SHAP are also illustrated to improve model interpretability by highlighting the most influential features, thereby aiding physician understanding. The novelty of this work lies in the integration of explainable AI with robust preprocessing and age-specific modeling across multiple ASD datasets, addressing both diagnostic accuracy and clinical interpretability. It can be considered that the suggested method can efficiently diagnose ASD at a very early stage and enhance the understanding of ASD diagnosis clinically.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3586314" target="_blank">https://dx.doi.org/10.1109/access.2025.3586314</a></p>2025-07-17T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3586314https://figshare.com/articles/journal_contribution/A_Clinically_Interpretable_Approach_for_Early_Detection_of_Autism_Using_Machine_Learning_With_Explainable_AI/30860177CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/308601772025-07-17T12:00:00Z |
| spellingShingle | A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI Oishi Jyoti (21593819) Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning Autism spectrum disorder (ASD) artificial intelligence (AI) machine learning (ML) explainable AI Accuracy Predictive models Autism Explainable AI Support vector machines Random forests Machine learning Machine learning algorithms Diseases Reliability |
| status_str | publishedVersion |
| title | A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI |
| title_full | A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI |
| title_fullStr | A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI |
| title_full_unstemmed | A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI |
| title_short | A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI |
| title_sort | A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI |
| topic | Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning Autism spectrum disorder (ASD) artificial intelligence (AI) machine learning (ML) explainable AI Accuracy Predictive models Autism Explainable AI Support vector machines Random forests Machine learning Machine learning algorithms Diseases Reliability |