{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T20:12:41Z","timestamp":1781381561444,"version":"3.54.1"},"reference-count":22,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T00:00:00Z","timestamp":1719705600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Science Reports"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Aims<\/jats:title><jats:p>Keratoconus is a progressive eye condition in which the normally round cornea thins and bulges outwards into a cone shape. This irregular shape causes light to scatter in multiple directions as it enters the eye, leading to distorted vision, increased sensitivity to light and frequent changes in the prescription of glasses or contact lenses. Detecting keratoconus at an early stage is not only difficult but also challenging.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The study has proposed an ensemble\u2010based machine learning (ML) technique named KeratoEL to detect keratoconus at an early stage. The proposed KeratoEL model combines the basic machine learning algorithms, namely support vector machine (SVM), decision tree (DT), random forest (RF) and artificial neural network (ANN). Before employing the ML model for keratoconus detection, the data set is first preprocessed manually by eliminating some features that don't contribute any significant value to predict the exact class. Moreover, the output features are labelled into three different classes and Extra Trees Classifier is used to find out the important features. Then, the features are sorted in descending order and top 45, 30, and 15 features are taken as input datasets against the output. Finally, different machine learning models are tested using the input datasets and performance metrics are measured<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed model obtains 98.0%, 98.9% and 99.8% accuracy for top 45, 30, and 15 number of features respectively. Overall experimental results show that the proposed ensemble model outperforms the existing machine learning models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed KeratoEL model effectively detects keratoconus at an early stage by combining SVM, DT, RF, and ANN algorithms, demonstrating superior performance over existing models. These results underscore the potential of the KeratoEL ensemble approach in enhancing early detection and treatment of keratoconus.<\/jats:p><\/jats:sec>","DOI":"10.1002\/hsr2.2202","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T04:40:00Z","timestamp":1719808800000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["KeratoEL: Detection of keratoconus using corneal parameters with ensemble learning"],"prefix":"10.1002","volume":"7","author":[{"given":"Prodeep Kumar","family":"Paul","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering Khulna University of Engineering &amp; Technology (KUET)  Khulna Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7533-8901","authenticated-orcid":false,"given":"Arif","family":"Hossan","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering Khulna University of Engineering &amp; Technology (KUET)  Khulna Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1571-2057","authenticated-orcid":false,"given":"Shah Muhammad A.","family":"Ullah","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering Khulna University of Engineering &amp; Technology (KUET)  Khulna Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,6,30]]},"reference":[{"key":"e_1_2_15_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0039-6257(97)00119-7"},{"key":"e_1_2_15_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clae.2021.101559"},{"key":"e_1_2_15_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40662-020-00225-z"},{"key":"e_1_2_15_5_1","doi-asserted-by":"publisher","DOI":"10.4103\/ijo.IJO_2111_20"},{"key":"e_1_2_15_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3016060"},{"key":"e_1_2_15_7_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0205998"},{"issue":"5","key":"e_1_2_15_8_1","first-page":"648","article-title":"Machine learning techniques in keratoconus classification: a systematic review","volume":"14","author":"Mustapha A","year":"2023","journal-title":"Mach Learn"},{"key":"e_1_2_15_9_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.opx.0000192350.01045.6f"},{"key":"e_1_2_15_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.548164"},{"key":"e_1_2_15_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.1993.342544"},{"key":"e_1_2_15_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/cem.873"},{"issue":"11","key":"e_1_2_15_13_1","first-page":"2290","article-title":"Current keratoconus detection methods compared with a neural network approach","volume":"38","author":"Smolek MK","year":"1997","journal-title":"Invest Ophthalmol Visual Sci"},{"key":"e_1_2_15_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0886-3350(00)00303-5"},{"key":"e_1_2_15_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2012.06.005"},{"key":"e_1_2_15_16_1","first-page":"14","article-title":"Detection of keratoconus and suspect keratoconus by machine vision","author":"Toutounchian F","year":"2012","journal-title":"Proceedings of the International MultiConference of Engineers and Computer Scientists"},{"key":"e_1_2_15_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2011.03.028"},{"key":"e_1_2_15_18_1","doi-asserted-by":"publisher","DOI":"10.1097\/ICO.0000000000000834"},{"key":"e_1_2_15_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2020.02.017"},{"key":"e_1_2_15_20_1","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8162567"},{"key":"e_1_2_15_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10792-022-02404-4"},{"key":"e_1_2_15_22_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2677521"},{"key":"e_1_2_15_23_1","article-title":"Replication data for the keratoconus severity identification using unsupervised machine learning Harvard dataverse 2018","author":"Yousefi S","year":"2018","journal-title":"PLOS One"}],"container-title":["Health Science Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/hsr2.2202","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T04:04:28Z","timestamp":1722053068000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/hsr2.2202"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,30]]},"references-count":22,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["10.1002\/hsr2.2202"],"URL":"https:\/\/doi.org\/10.1002\/hsr2.2202","archive":["Portico"],"relation":{},"ISSN":["2398-8835","2398-8835"],"issn-type":[{"value":"2398-8835","type":"print"},{"value":"2398-8835","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,30]]},"assertion":[{"value":"2023-11-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e2202"}}