{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T19:42:38Z","timestamp":1769197358690,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T00:00:00Z","timestamp":1728604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Multimedia University Research Fellow","award":["MMUI\/240021"],"award-info":[{"award-number":["MMUI\/240021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Automated Machine Learning (AutoML) enhances productivity and efficiency by automating the entire process of machine learning model development, from data preprocessing to model deployment. These tools are accessible to users with varying levels of expertise and enable efficient, scalable, and accurate classification across different applications. This paper evaluates two popular AutoML tools, the Tree-Based Pipeline Optimization Tool (TPOT) version 0.10.2 and Konstanz Information Miner (KNIME) version 5.2.5, comparing their performance in a classification task. Specifically, this work analyzes autism spectrum disorder (ASD) detection in toddlers as a use case. The dataset for ASD detection was collected from various rehabilitation centers in Pakistan. TPOT and KNIME were applied to the ASD dataset, with TPOT achieving an accuracy of 85.23% and KNIME achieving 83.89%. Evaluation metrics such as precision, recall, and F1-score validated the reliability of the models. After selecting the best models with optimal accuracy, the most important features for ASD detection were identified using these AutoML tools. The tools optimized the feature selection process and significantly reduced diagnosis time. This study demonstrates the potential of AutoML tools and feature selection techniques to improve early ASD detection and outcomes for affected children and their families.<\/jats:p>","DOI":"10.3390\/info15100625","type":"journal-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T03:53:47Z","timestamp":1728618827000},"page":"625","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Comparative Analysis of Automated Machine Learning Tools: A Use Case for Autism Spectrum Disorder Detection"],"prefix":"10.3390","volume":"15","author":[{"given":"Rana Tuqeer","family":"Abbas","sequence":"first","affiliation":[{"name":"Department of Software Engineering, Bahria University Islamabad, Islamabad 44000, Pakistan"}]},{"given":"Kashif","family":"Sultan","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Bahria University Islamabad, Islamabad 44000, Pakistan"}]},{"given":"Muhammad","family":"Sheraz","sequence":"additional","affiliation":[{"name":"Centre for Wireless Technology, Faculty of Engineering, Multimedia University, Cyberjaya 63100, Selangor, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6285-9481","authenticated-orcid":false,"given":"Teong Chee","family":"Chuah","sequence":"additional","affiliation":[{"name":"Centre for Wireless Technology, Faculty of Engineering, Multimedia University, Cyberjaya 63100, Selangor, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15781","DOI":"10.1007\/s00521-022-07266-6","article-title":"An application of machine learning regression to feature selection: A study of logistics performance and economic attribute","volume":"34","author":"Jomthanachai","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_2","unstructured":"Abdallah, T.A., and de La Iglesia, B. (2015). Survey on Feature Selection. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","article-title":"Feature selection in machine learning: A new perspective","volume":"300","author":"Cai","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_4","unstructured":"Roffo, G. (2016). Feature selection library (MATLAB toolbox). arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6330002","DOI":"10.1155\/2023\/6330002","article-title":"Feature signature discovery for autism detection: An automated machine learning based feature ranking framework","volume":"2023","author":"Jacob","year":"2023","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sharma, A.A., and Dey, S. (2012, January 23\u201326). A comparative study of feature selection and machine learning techniques for sentiment analysis. Proceedings of the 2012 ACM Research in Applied Computation Symposium, San Antonio, TX, USA.","DOI":"10.1145\/2401603.2401605"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Aksu, D.D., \u00dcstebay, S., Aydin, M.A., and Atmaca, T. (2018). Intrusion detection with comparative analysis of supervised learning techniques and fisher score feature selection algorithm. Computer and Information Sciences, Proceedings of the 32nd International Symposium, ISCIS 2018, the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, 20\u201321 September 2018, Springer International Publishing. Proceedings 32.","DOI":"10.1007\/978-3-030-00840-6_16"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kuzhippallil, M.A., Joseph, C., and Kannan, A. (2020, January 6\u20137). Comparative analysis of machine learning techniques for indian liver disease patients. Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS48705.2020.9074368"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1002\/ima.22316","article-title":"Comparative analysis of Alzheimer\u2019s disease classification by CDR level using CNN, feature selection, and machine-learning techniques","volume":"29","author":"Khagi","year":"2019","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_10","first-page":"18715","article-title":"Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning","volume":"53","author":"Mafarja","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, K.Y., Sampaio de Lima, R., Burnside, N.G., Vahtm\u00e4e, E., Kutser, T., Sepp, K., and Sepp, K. (2022). Toward automated machine learning-based hyperspectral image analysis in crop yield and biomass estimation. Remote Sens., 14.","DOI":"10.3390\/rs14051114"},{"key":"ref_12","unstructured":"Adla, Y.A.A., Raydan, D.G., Charaf, M.Z.J., Saad, R.A., Nasreddine, J., and Diab, M.O. (2021, January 7\u20139). Automated detection of polycystic ovary syndrome using machine learning techniques. Proceedings of the 2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME), Werdanyeh, Lebanon."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1016\/j.procs.2020.03.399","article-title":"Analysis and detection of autism spectrum disorder using machine learning techniques","volume":"167","author":"Raj","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Romero-Garc\u00eda, R., Mart\u00ednez-Tom\u00e1s, R., Pozo, P., de la Paz, F., and Sarri\u00e1, E. (2021). Q-CHAT-NAO: A robotic approach to autism screening in toddlers. J. Biomed. Inform., 118.","DOI":"10.1016\/j.jbi.2021.103797"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1177\/1460458218796636","article-title":"An accessible and efficient autism screening method for behavioural data and predictive analyses","volume":"25","author":"Thabtah","year":"2019","journal-title":"Health Inform. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.jaac.2011.11.003","article-title":"Toward brief \u201cred flags\u201d for autism screening: The short autism spectrum quotient and the short quantitative checklist in 1,000 cases and 3,000 controls","volume":"51","author":"Allison","year":"2012","journal-title":"J. Am. Acad. Child Adolesc. Psychiatry"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1414","DOI":"10.1007\/s10803-007-0509-7","article-title":"The Q-CHAT (Quantitative CHecklist for Autism in Toddlers): A normally distributed quantitative measure of autistic traits at 18\u201324 months of age: Preliminary report","volume":"38","author":"Allison","year":"2008","journal-title":"J. Autism Dev. Disord."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ruta, L., Chiarotti, F., Arduino, G.M., Apicella, F., Leonardi, E., Maggio, R., Carrozza, C., Chericoni, N., Costanzo, V., and Turco, N. (2019). Validation of the quantitative checklist for autism in toddlers in an Italian clinical sample of young children with autism and other developmental disorders. Front. Psychiatry, 10.","DOI":"10.3389\/fpsyt.2019.00488"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/1866-1955-6-12","article-title":"Robust features for the automatic identification of autism spectrum disorder in children","volume":"6","author":"Eldridge","year":"2014","journal-title":"J. Neurodev. Disord."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101760","DOI":"10.1016\/j.rasd.2021.101760","article-title":"Quantitative Checklist for Autism in Toddlers (Q-CHAT): A psychometric study with Serbian Toddlers","volume":"83","year":"2021","journal-title":"Res. Autism Spectr. Disord."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Islam, S., Akter, T., Zakir, S., Sabreen, S., and Hossain, M.I. (2020, January 16\u201318). Autism spectrum disorder detection in toddlers for early diagnosis using machine learning. Proceedings of the 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia.","DOI":"10.1109\/CSDE50874.2020.9411531"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1002\/aur.2033","article-title":"A review of screening tools for the identification of autism spectrum disorders and developmental delay in infants and young children: Recommendations for use in low-and middle-income countries","volume":"12","author":"Marlow","year":"2019","journal-title":"Autism Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Farooqi, N., Bukhari, F., and Iqbal, W. (2021, January 13\u201314). Predictive analysis of autism spectrum disorder (ASD) using machine learning. Proceedings of the 2021 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan.","DOI":"10.1109\/FIT53504.2021.00063"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cerrada, M., Trujillo, L., Hern\u00e1ndez, D.E., Correa Zevallos, H.A., Macancela, J.C., Cabrera, D., and Vinicio S\u00e1nchez, R. (2022). AutoML for feature selection and model tuning applied to fault severity diagnosis in spur gearboxes. Math. Comput. Appl., 27.","DOI":"10.3390\/mca27010006"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1192\/bjp.161.6.839","article-title":"Can autism be detected at 18 months?: The needle, the haystack, and the CHAT","volume":"161","author":"Allen","year":"1992","journal-title":"Br. J. Psychiatry"},{"key":"ref_26","unstructured":"(2020, January 01). Available online: https:\/\/epistasislab.github.io\/tpot\/."},{"key":"ref_27","unstructured":"Olson, R.S., and Moore, J.H. (2016, January 24). TPOT: A tree-based pipeline optimization tool for automating machine learning. Proceedings of the Workshop on Automatic Machine Learning, New York, NY, USA."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/10\/625\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:11:02Z","timestamp":1760112662000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/10\/625"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,11]]},"references-count":27,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["info15100625"],"URL":"https:\/\/doi.org\/10.3390\/info15100625","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,11]]}}}