Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing
Purpose: Our study explored the use of artificial intelligence (AI) to diagnose autism spectrum disorder (ASD). It focused on machine learning (ML) and deep learning (DL) to detect ASD from text inputs on social media, addressing challenges in traditional ASD diagnosis. Methods: We used natural lang...
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| creator | Rubio-Martín, Sergio García-Ordás, María Teresa Bayón-Gutiérrez, Martín Prieto-Fernández, Natalia Benítez-Andrades, José Alberto |
| description | Purpose: Our study explored the use of artificial intelligence (AI) to diagnose autism spectrum disorder (ASD). It focused on machine learning (ML) and deep learning (DL) to detect ASD from text inputs on social media, addressing challenges in traditional ASD diagnosis. Methods: We used natural language processing (NLP), ML, and DL models (including decision trees, XGB, KNN, RNN, LSTM, Bi-LSTM, BERT, and BERTweet) to analyze 404,627 tweets, classifying them based on ASD or non-ASD authors. A subset of 90,000 tweets was used for model training and testing. Results: Our AI models showed high accuracy, with an 88% success rate in identifying texts from individuals with ASD. Conclusion: The study demonstrates AI's potential in improving ASD diagnosis, especially in children, highlighting the importance of early detection. |
| doi_str_mv | 10.48550/arxiv.2403.03581 |
| format | Article |
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It focused on machine learning (ML) and deep learning (DL) to detect ASD from text inputs on social media, addressing challenges in traditional ASD diagnosis. Methods: We used natural language processing (NLP), ML, and DL models (including decision trees, XGB, KNN, RNN, LSTM, Bi-LSTM, BERT, and BERTweet) to analyze 404,627 tweets, classifying them based on ASD or non-ASD authors. A subset of 90,000 tweets was used for model training and testing. Results: Our AI models showed high accuracy, with an 88% success rate in identifying texts from individuals with ASD. 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Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://creativecommons.org/licenses/by-nc-nd/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,780,881,27902</link.rule.ids><backlink>$$Uhttps://doi.org/10.48550/arXiv.2403.03581$$DView paper in arXiv$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.1007/s13755-024-00281-y$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink></links><search><creatorcontrib>Rubio-Martín, Sergio</creatorcontrib><creatorcontrib>García-Ordás, María Teresa</creatorcontrib><creatorcontrib>Bayón-Gutiérrez, Martín</creatorcontrib><creatorcontrib>Prieto-Fernández, Natalia</creatorcontrib><creatorcontrib>Benítez-Andrades, José Alberto</creatorcontrib><title>Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing</title><title>arXiv.org</title><description>Purpose: Our study explored the use of artificial intelligence (AI) to diagnose autism spectrum disorder (ASD). 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Conclusion: The study demonstrates AI's potential in improving ASD diagnosis, especially in children, highlighting the importance of early detection.</description><subject>Accuracy</subject><subject>Artificial intelligence</subject><subject>Autism</subject><subject>Computer Science - Computation and Language</subject><subject>Computer Science - Learning</subject><subject>Decision analysis</subject><subject>Decision trees</subject><subject>Deep learning</subject><subject>Diagnosis</subject><subject>Machine learning</subject><subject>Natural language processing</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><sourceid>GOX</sourceid><recordid>eNpFUN1LwzAcDILgmPsDfDLgc2c-mrbxbcz5AQMf3Hv5JU22jjatSavu2X_cbBN8uYPj7jgOoRtK5mkhBLkH_11_zllK-JxwUdALNGGc06RIGbtCsxD2hBCW5UwIPkE_K7cDp2u3xYv3R1yZweih7hwGrUcP-vCAAeuuVbUzFYa-9x3oHe4sbiNHETcGvDvmwVUxb_p_pe0q0wT8VQ877GCIfQ1uwG1H2Bocm7QJIfqu0aWFJpjZH0_R5mm1Wb4k67fn1-VinYBgLFGcaQVEiUJWOaS5VBbAVsTQjFLILFdaZVblipGMFByYzLTMCc-lTEHklk_R7bn29FDZ-7oFfyiPT5Wnp6Lj7uyI2z5GE4Zy343exU0lkymlPALjv4QFbb0</recordid><startdate>20240306</startdate><enddate>20240306</enddate><creator>Rubio-Martín, Sergio</creator><creator>García-Ordás, María Teresa</creator><creator>Bayón-Gutiérrez, Martín</creator><creator>Prieto-Fernández, Natalia</creator><creator>Benítez-Andrades, José Alberto</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20240306</creationdate><title>Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing</title><author>Rubio-Martín, Sergio ; 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It focused on machine learning (ML) and deep learning (DL) to detect ASD from text inputs on social media, addressing challenges in traditional ASD diagnosis. Methods: We used natural language processing (NLP), ML, and DL models (including decision trees, XGB, KNN, RNN, LSTM, Bi-LSTM, BERT, and BERTweet) to analyze 404,627 tweets, classifying them based on ASD or non-ASD authors. A subset of 90,000 tweets was used for model training and testing. Results: Our AI models showed high accuracy, with an 88% success rate in identifying texts from individuals with ASD. Conclusion: The study demonstrates AI's potential in improving ASD diagnosis, especially in children, highlighting the importance of early detection.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2403.03581</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Artificial intelligence Autism Computer Science - Computation and Language Computer Science - Learning Decision analysis Decision trees Deep learning Diagnosis Machine learning Natural language processing |
| title | Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing |
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