{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:59:22Z","timestamp":1743145162589,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031477232"},{"type":"electronic","value":"9783031477249"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-47724-9_39","type":"book-chapter","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T20:29:08Z","timestamp":1713472148000},"page":"591-609","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Autism Spectrum Disorder Detecting Mechanism on\u00a0Social Communication Skills Using Machine Learning Approaches"],"prefix":"10.1007","author":[{"given":"Dipto","family":"Biswas","sequence":"first","affiliation":[]},{"given":"Md.","family":"Samsuddoha","sequence":"additional","affiliation":[]},{"given":"Md.","family":"Erfan","sequence":"additional","affiliation":[]},{"given":"Rahat Hossain","family":"Faisal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,19]]},"reference":[{"key":"39_CR1","doi-asserted-by":"publisher","unstructured":"Guangqi, W., Cao, P., Huiwen, B., Wenju, Y., Tong, Z., Osmar, Z.: MVS-GCN: a prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis, In: Computers in Biology and Medicine, vol. 142, pp. 105239 (2022). https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105239","DOI":"10.1016\/j.compbiomed.2022.105239"},{"key":"39_CR2","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.ijpsycho.2022.01.004","volume":"173","author":"S Ozdemir","year":"2022","unstructured":"Ozdemir, S., Isik, A.-B., Ibrahim, K., Suat, O.: Development of a visual attention based decision support system for autism spectrum disorder screening. Int. J. Psychophysiol. 173, 69\u201381 (2022)","journal-title":"Int. J. Psychophysiol."},{"key":"39_CR3","doi-asserted-by":"crossref","unstructured":"Nishat, M.-M., Fahim, F., Tasnimul, H., Sarker, M.-N., Afsana, H.-B., Minhajul, I.-S., Ashraful, H.: Detection of autism spectrum disorder by discriminant analysis algorithm. In: Proceedings of the International Conference on Big Data, IoT, and Machine Learning, pp. 473\u2013482. Springer, Singapore (2022)","DOI":"10.1007\/978-981-16-6636-0_36"},{"key":"39_CR4","unstructured":"Buxbaum, J.-D.: Multiple rare variants in the etiology of autism spectrum disorders. In: Dialogues in Clinical Neuroscience (2022)"},{"key":"39_CR5","unstructured":"Chandrasekhar, T., Linmarie, S.: Challenges in the diagnosis and treatment of depression in autism spectrum disorders across the lifespan. In: Dialogues in Clinical Neuroscience (2022)"},{"key":"39_CR6","unstructured":"Alqaysi, M.-E., Albahri, A.-S., Rula, A.-H.: Diagnosis-based hybridization of multimedical tests and sociodemographic characteristics of autism spectrum disorder using artificial intelligence and machine learning techniques: a systematic review. Int. J. Telemed. Appl"},{"key":"39_CR7","doi-asserted-by":"crossref","unstructured":"Tang, X., Zihui, H., Jiayin, X., Li, Y.-Z.-J., Liyang, Z., Xing, S.: Verbal fluency as a predictor of autism spectrum disorder diagnosis and co-occurring attention-deficit\/hyperactivity disorder symptoms. In: Reading and Writing, pp. 1\u201325 (2022)","DOI":"10.1007\/s11145-022-10319-w"},{"key":"39_CR8","unstructured":"Fernandez, B.-A., Stephen, W.-S.: Syndromic autism spectrum disorders: moving from a clinically defined to a molecularly defined approach. In: Dialogues in Clinical Neuroscience (2022)"},{"key":"39_CR9","doi-asserted-by":"crossref","unstructured":"Shu, L.-O., Jahmunah, V., Arunkumar, N., Enas, W., Abdulhay, R., Gururajan, N.-A., Edward, J., Ciaccio, K.-H.-C., Rajendra, U.-A.: A novel automated autism spectrum disorder detection system. In: Complex & Intelligent Systems 7, vol. 5, pp. 2399\u20132413 (2021)","DOI":"10.1007\/s40747-021-00408-8"},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"Bhola, J., Rubal, J., Malik, M.-M.-J., Shadab, A.-P.: Machine learning techniques for analysing and identifying autism spectrum disorder. In: Artificial Intelligence for Accurate Analysis and Detection of Autism Spectrum Disorder, pp. 69\u201381. IGI Global (2021)","DOI":"10.4018\/978-1-7998-7460-7.ch005"},{"key":"39_CR11","doi-asserted-by":"publisher","unstructured":"Thabtah, F. Peebles, D.: A new machine learning model based on induction of rules for autism detection. Health Inform. J. (2019). Art. no. 1460458218824711, https:\/\/doi.org\/10.1177\/1460458218824711","DOI":"10.1177\/1460458218824711"},{"key":"39_CR12","doi-asserted-by":"crossref","unstructured":"Satu, M.-S., Sathi, F.-F., Arifen, M.-S., Ali, M.-H. Moni, M.-A.: Early detection of autism by extracting features: a case study in Bangladesh. In: International Conference on Robotics, Electrical and Signal Processing Technique (ICREST), pp. 87\u201390 (2019)","DOI":"10.1109\/ICREST.2019.8644357"},{"issue":"8","key":"39_CR13","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1093\/jamia\/ocy039","volume":"25","author":"H Abbas","year":"2018","unstructured":"Abbas, H., Garberson, F., Glover, E., Wall, D.-P.: Machine learning approach for early detection of autism by combining questionnaire and home video screening. J. Am. Med. Informat. Assoc. 25(8), 1000\u20131007 (2018)","journal-title":"J. Am. Med. Informat. Assoc."},{"key":"39_CR14","doi-asserted-by":"crossref","unstructured":"Thabtah, F.: Autism spectrum disorder screening: machine learning adaptation and DSM-5 fulfillment. In: Proceedings of the 2nd International Conference on Medical and Health Informatics, pp. 1\u20136 (2017)","DOI":"10.1145\/3107514.3107515"},{"key":"39_CR15","doi-asserted-by":"publisher","unstructured":"Hossain, M.-A., Islam, S.-M.-S., Quinn, J.-M., Huq, F., Moni, M.-A.: Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality. J. Biomed. Inform. 100, 103313. https:\/\/doi.org\/10.1016\/j.jbi.2019.103313","DOI":"10.1016\/j.jbi.2019.103313"},{"issue":"2","key":"39_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/tp.2015.221","volume":"6","author":"M Duda","year":"2016","unstructured":"Duda, M., Ma, R., Haber, N., Wall, D.-P.: Use of machine learning for behavioral distinction of autism and ADHD. Transl. Psychiatry 6(2), e732 (2016)","journal-title":"Transl. Psychiatry"},{"issue":"7","key":"39_CR17","doi-asserted-by":"publisher","first-page":"2146","DOI":"10.1007\/s10803-015-2379-8","volume":"45","author":"A Crippa","year":"2015","unstructured":"Crippa, A., Salvatore, C., Perego, P., Forti, S., Nobile, M., Molteni, M., Castiglioni, I.: Use of machine learning to identify children with autism and their motor abnormalities. J. Autism Develop. Disorders 45(7), 2146\u20132156 (2015)","journal-title":"J. Autism Develop. Disorders"},{"key":"39_CR18","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ijmedinf.2018.06.009","volume":"117","author":"F Thabtah","year":"2018","unstructured":"Thabtah, F., Kamalov, F., Rajab, K.: A new computational intelligence approach to detect autistic features for autism screening. Int. J. Med. Inform. 117, 112\u2013124 (2018)","journal-title":"Int. J. Med. Inform."},{"issue":"2","key":"39_CR19","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.jaac.2011.11.003","volume":"51","author":"C Allison","year":"2012","unstructured":"Allison, C., Auyeung, B., Baron, C.-S.: Toward brief \u2018red flags\u2019 for autism screening: The short autism spectrum quotient and the short quantitative checklist in 1,000 cases and 3,000 controls. J. Am. Acad. Child Adolesc. Psychiatry 51(2), 202\u2013212 (2012)","journal-title":"J. Am. Acad. Child Adolesc. Psychiatry"},{"key":"39_CR20","doi-asserted-by":"publisher","first-page":"166509","DOI":"10.1109\/ACCESS.2019.2952609","volume":"7","author":"T Akter","year":"2019","unstructured":"Akter, T., Shahriare, M.-S., Imran, M.-K., Mohammad, H.-A., Shahadat, U., Pietro, L., Julian, M.-Q., Mohammad, A.-M.: Machine learning-based models for early stage detection of autism spectrum disorders. IEEE Access 7, 166509\u2013166527 (2019)","journal-title":"IEEE Access"},{"key":"39_CR21","doi-asserted-by":"crossref","unstructured":"Satu, M.-S., Ahamed, S., Hossain, F., Akter, T., Farid, D.-M.: Mining traffic accident data of N5 national highway in bangladesh employing decision trees. In: Proceedings of IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp. 722\u2013725 (2017)","DOI":"10.1109\/R10-HTC.2017.8289059"},{"key":"39_CR22","doi-asserted-by":"crossref","unstructured":"Satu, M.-S., Tasnim, F., Akter, T., Halder, S.: Exploring significant heart disease factors based on semi supervised learning algorithms. In: Proceedings of International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), pp. 1\u20134 (2018)","DOI":"10.1109\/IC4ME2.2018.8465642"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47724-9_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T20:40:34Z","timestamp":1713472834000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47724-9_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031477232","9783031477249"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47724-9_39","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"19 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}