{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:55:56Z","timestamp":1781283356289,"version":"3.54.1"},"reference-count":23,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["THC"],"published-print":{"date-parts":[[2021,1,15]]},"abstract":"<jats:p>BACKGROUND: Pleural Mesothelioma (PM) is an unusual, belligerent tumor that rapidly develops into cancer in the pleura of the lungs. Pleural Mesothelioma is a common type of Mesothelioma that accounts for about 75% of all Mesothelioma diagnosed yearly in the U.S. Diagnosis of Mesothelioma takes several months and is expensive. Given the risk and constraints associated with PM diagnosis, early identification of this ailment is essential for patient health. OBJECTIVE: In this study, we use artificial intelligence algorithms recommending the best fit model for early diagnosis and prognosis of Malignant Pleural Mesothelioma (MPM). METHODS: We retrospectively retrieved patients\u2019 clinical data collected by Dicle University, Turkey and applied multilayered perceptron (MLP), voted perceptron (VP), Clojure classifier (CC), kernel logistic regression (KLR), stochastic gradient decent (SGD), adaptive boosting (AdaBoost), Hoeffding tree (VFDT), and primal estimated sub-gradient solver for support vector machine (s-Pegasos). We evaluated the models, compared and tested them using paired t-test (corrected) at 0.05 significance based on their respective classification accuracy, f-measure, precision, recall, root mean squared error, receivers\u2019 characteristic curve (ROC), and precision-recall curve (PRC). RESULTS: In phase 1, SGD, AdaBoost.M1, KLR, MLP, VFDT generate optimal results with the highest possible performance measures. In phase 2, AdaBoost, with a classification accuracy of 71.29%, outperformed all other algorithms. C-reactive protein, platelet count, duration of symptoms, gender, and pleural protein were found to be the most relevant predictors that can prognosticate Mesothelioma. CONCLUSION: This study confirms that data obtained from biopsy and imaging tests are strong predictors of Mesothelioma but are associated with a high cost; however, they can identify Mesothelioma with optimal accuracy.<\/jats:p>","DOI":"10.3233\/thc-202237","type":"journal-article","created":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T12:12:43Z","timestamp":1592568763000},"page":"45-58","source":"Crossref","is-referenced-by-count":37,"title":["Predicting cancer using supervised machine learning: Mesothelioma"],"prefix":"10.1177","volume":"29","author":[{"given":"Avishek","family":"Choudhury","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"issue":"10","key":"10.3233\/THC-202237_ref1","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1038\/s41591-019-0583-3","article-title":"Deep learning-based classification of mesothelioma improves prediction of patient outcome","volume":"25","author":"Courtiol","year":"2019","journal-title":"Nature Medicine"},{"issue":"14","key":"10.3233\/THC-202237_ref2","doi-asserted-by":"crossref","first-page":"2636","DOI":"10.1200\/JCO.2003.11.136","article-title":"Phase III study of pemetrexed in combination with cisplatin versus cisplatin alone in patients with malignant pleural mesothelioma","volume":"21","author":"Vogelzang","year":"2003","journal-title":"Journal of Clinical Oncology"},{"issue":"10026","key":"10.3233\/THC-202237_ref3","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1016\/S0140-6736(15)01238-6","article-title":"Bevacizumab for newly diagnosed pleural mesothelioma in the Mesothelioma Avastin Cisplatin Pemetrexed Study (MAPS): a randomised, controlled, open-label, phase 3 trial","volume":"387","author":"Zalcman","year":"2016","journal-title":"The Lancet"},{"issue":"12","key":"10.3233\/THC-202237_ref4","doi-asserted-by":"crossref","first-page":"2082","DOI":"10.1016\/j.jtho.2016.09.123","article-title":"The IASLC mesothelioma staging project: improving staging of a rare disease through international participation","volume":"11","author":"Pass","year":"2016","journal-title":"Journal of Thoracic Oncology"},{"issue":"8","key":"10.3233\/THC-202237_ref5","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1016\/j.jtho.2016.04.027","article-title":"North American multicenter volumetric CT study for clinical staging of malignant pleural mesothelioma: feasibility and logistics of setting up a quantitative imaging study","volume":"11","author":"Gill","year":"2016","journal-title":"Journal of Thoracic Oncology"},{"issue":"1","key":"10.3233\/THC-202237_ref6","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1183\/09031936.00146110","article-title":"Volumetry: an alternative to assess therapy response for malignant pleural mesothelioma","volume":"38","author":"Frauenfelder","year":"2011","journal-title":"European Respiratory Journal"},{"issue":"4","key":"10.3233\/THC-202237_ref7","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1038\/ng.3520","article-title":"Comprehensive genomic analysis of malignant pleural mesothelioma identifies recurrent mutations, gene fusions and splicing alterations","volume":"48","author":"Bueno","year":"2016","journal-title":"Nature Genetics"},{"issue":"9","key":"10.3233\/THC-202237_ref8","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1183\/09031936.96.09091932","article-title":"The epidemiology of mesothelioma in historical context","volume":"9","author":"McDonald","year":"1996","journal-title":"European Respiratory Journal"},{"issue":"11","key":"10.3233\/THC-202237_ref9","doi-asserted-by":"crossref","first-page":"234","DOI":"10.21037\/atm.2017.03.74","article-title":"Environmental asbestos exposure and risk of mesothelioma","volume":"5","author":"Noonan","year":"2017","journal-title":"Annals of Translational Medicine"},{"issue":"1","key":"10.3233\/THC-202237_ref10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1179\/2049396712Y.0000000016","article-title":"Mesothelioma incidence in 50 states and the District of Columbia, United States, 2003\u20132008","volume":"19","author":"Jane Henley","year":"2013","journal-title":"International Journal of Occupational and Environmental Health"},{"issue":"5","key":"10.3233\/THC-202237_ref11","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1002\/ajim.4700200507","article-title":"The incidence of malignant mesothelioma in Australia 1982\u20131988","volume":"20","author":"Leigh","year":"1991","journal-title":"American Journal of Industrial Medicine"},{"key":"10.3233\/THC-202237_ref18","doi-asserted-by":"crossref","unstructured":"Ilhan HO, Celik E. 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