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Assessing the performance of healthcare services is a complex task due to the wide range of health conditions among patients, making it difficult to establish consistent evaluation metrics. The study employs a support vector machine (SVM) regression model to address this issue, which helps analyze and manage patient risk factors effectively. To enhance the monitoring of healthcare service quality, this study introduces a new SVM\u2010based adaptive risk\u2010adjusted exponentially weighted moving average (SVM\u2010ARAEWMA) control chart. This chart is designed using the residuals generated by the SVM regression model, allowing for a more accurate assessment of variations in patient outcomes. This study demonstrates how SVM effectively assesses patient conditions by applying this methodology to real\u2010world data from cardiac surgery patients. Compared to traditional risk\u2010adjusted exponentially weighted moving average (RAEWMA) and adaptive RAEWMA (ARAEWMA) control methods, the SVM\u2010ARAEWMA chart is more effective in identifying critical changes in healthcare quality and patient outcomes.<\/jats:p>","DOI":"10.1002\/qre.3797","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T03:39:18Z","timestamp":1745897958000},"page":"2567-2575","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Machine Learning Approach to Adaptive EWMA Control Charts: Insights From Cardiac Surgery Data"],"prefix":"10.1002","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5208-5374","authenticated-orcid":false,"given":"Tahir","family":"Abbas","sequence":"first","affiliation":[{"name":"Department of Mathematics College of Sciences University of Sharjah  Sharjah United Arab Emirates"}]},{"given":"Fahad R.","family":"Albogamy","sequence":"additional","affiliation":[{"name":"Computer Sciences Program Department of Mathematics Turabah University College Taif University  Taif Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8996-1120","authenticated-orcid":false,"given":"Muhammad","family":"Abid","sequence":"additional","affiliation":[{"name":"Department of Statistics Government College University  Faisalabad Pakistan"}]}],"member":"311","published-online":{"date-parts":[[2025,4,29]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/16843703.2016.1208932"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2021.107568"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.2818"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/00949655.2020.1814775"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/03610918.2019.1667393"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.3195"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.3301"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/03610918.2022.2155315"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.2891"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.3085"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-08257-x"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.2044"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8104"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.1722"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-60285-2"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-45070-x"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.3696"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.3728"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-022-06803-8"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.2272"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2019.04.005"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2347736.2347755"},{"key":"e_1_2_10_24_1","doi-asserted-by":"crossref","unstructured":"C.RudinandD.Carlson \u201cThe secrets of machine learning: Ten things you wish you had known earlier to be more effective at data analysis \u201d inOperations research & management science in the age of analytics INFORMS(2019) 44\u201372.","DOI":"10.1287\/educ.2019.0200"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21061999"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022627411411"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009715923555"},{"key":"e_1_2_10_28_1","article-title":"Support Vector Method for Function Approximation, Regression Estimation and Signal Processing","volume":"9","author":"Vapnik V.","year":"1996","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_10_29_1","volume-title":"Support Vector Machines for Classification and Regression","author":"Gunn S. 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