1. What are the contributions in this paper?
Avishek Choudhury et al. this paper presented a research assistant at the School of Systems and Enterprises at the Stevens Institute of Technology.
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Predicting cancer using supervised machine learning: Mesothelioma
TL;DR: C-reactive protein, platelet count, duration of symptoms, gender, and pleural protein were found to be the most relevant predictors that can prognosticate Mesothelioma.
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Abstract: 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 US 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' 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 005 significance based on their respective classification accuracy, f-measure, precision, recall, root mean squared error, receivers' characteristic curve (ROC), and precision-recall curve (PRC) Results In phase 1, SGD, AdaBoostM1, KLR, MLP, VFDT generate optimal results with the highest possible performance measures In phase 2, AdaBoost, with a classification accuracy of 7129%, 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
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Old Borders and New Horizons in Multimodality Imaging of Malignant Pleural Mesothelioma.
TL;DR: In this paper, various imaging techniques involved in detection, staging, and preoperative planning in malignant pleural mesothelioma (MPM) focusing on new imaging modalities are described.
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