DeepAI AI Chat
Log In Sign Up

Predicting Cancer Using Supervised Machine Learning: Mesothelioma

by   Avishek Choudhury, et al.

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 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 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 using paired T-test (corrected) at 0.05 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, 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 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 imagining tests are strong predictors of Mesothelioma but are associated with a high cost; however, they can identify Mesothelioma with optimal accuracy.


page 3

page 6

page 9


Identification of Cancer - Mesothelioma Disease Using Logistic Regression and Association Rule

Malignant Pleural Mesothelioma (MPM) or malignant mesothelioma (MM) is a...

Application of computer simulation results and machine learning in analysis of microwave radiothermometry data

This work was done with the aim of developing the fundamental breast can...

Breast and Colon Cancer Classification from Gene Expression Profiles Using Data Mining Techniques

Early detection of cancer increases the probability of recovery. This pa...

Computer Aided Diagnosis for Spitzoid lesions classification using Artificial Intelligence techniques

Spitzoid lesions may be largely categorized into Spitz Nevus, Atypical S...

A Methodology for Customizing Clinical Tests for Esophageal Cancer based on Patient Preferences

Tests for Esophageal cancer can be expensive, uncomfortable and can have...