{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T04:43:23Z","timestamp":1782967403954,"version":"3.54.5"},"reference-count":58,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Princess Nourah bint Abdulrahman University Researchers Supporting Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia","award":["PNURSP2024R444"],"award-info":[{"award-number":["PNURSP2024R444"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Searching for a reliable indicator of treatment response in sarcoidosis remains a challenge. The use of the soluble interleukin 2 receptor (sIL-2R) as a measure of disease activity has been proposed by researchers. A machine learning model was aimed to be developed in this study to predict sIL-2R levels based on a patient\u2019s serum angiotensin-converting enzyme (ACE) levels, potentially aiding in lung function evaluation. A novel forecasting model (SVR-BE-CO) for sIL-2R prediction is introduced, which combines support vector regression (SVR) with a hybrid optimization model (BES-CO); The hybrid optimization model composed of Bald Eagle Optimizer (BES) and Chimp Optimizer (CO) model. In this forecasting model, the hyper-parameters of the SVR model are optimized by the BES-CO hybrid optimization model, ultimately improving the accuracy of the predicted sIL-2R values. The hybrid forecasting model SVR-BE-CO model was evaluated against various forecasting methods, including Hybrid SVR with Firefly Algorithm (SVR-FFA), decision tree (DT), SVR with Gray Wolf Optimization (SVR-GWO) and random forest (RF). It was demonstrated that the hybrid SVR-BE-CO model surpasses all other methods in terms of accuracy.<\/jats:p>","DOI":"10.7717\/peerj-cs.2455","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T08:44:52Z","timestamp":1733388292000},"page":"e2455","source":"Crossref","is-referenced-by-count":3,"title":["Enhanced diagnosing patients suspected of sarcoidosis using a hybrid support vector regression model with bald eagle and chimp optimizers"],"prefix":"10.7717","volume":"10","author":[{"given":"Guogang","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, Shanghai General Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hani","family":"Attar","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Zarqa University, Zarqa, 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