Predicting cancer using supervised machine learning: Mesothelioma
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' 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' 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.
Insights
Artificial intelligence models can aid in the early diagnosis of Malignant Pleural Mesothelioma (MPM). Adaptive Boosting (AdaBoost) demonstrated the highest accuracy, identifying key predictors for Mesothelioma prognosis.
Area of Science:
- Oncology
- Medical Informatics
- Machine Learning
Background:
- Malignant Pleural Mesothelioma (MPM) is a rare but aggressive lung cancer.
- Early diagnosis is crucial for patient outcomes but current methods are time-consuming and costly.
- MPM accounts for approximately 75% of all Mesothelioma cases in the U.S.
Purpose of the Study:
- To identify the optimal artificial intelligence (AI) model for early diagnosis and prognosis of Malignant Pleural Mesothelioma (MPM).
- To compare the performance of various machine learning algorithms in predicting MPM.
Main Methods:
- Retrospective analysis of clinical data from Dicle University, Turkey.
- Application and evaluation of multiple AI algorithms including multilayered perceptron (MLP), kernel logistic regression (KLR), and adaptive boosting (AdaBoost).
- Model comparison using paired t-tests based on classification accuracy, f-measure, precision, recall, ROC, and PRC.
Main Results:
- Several AI models, including SGD, AdaBoost.M1, KLR, MLP, and VFDT, showed optimal performance in initial phases.
- Adaptive Boosting (AdaBoost) achieved the highest classification accuracy at 71.29% in the second phase.
- Key predictors for MPM prognosis identified include C-reactive protein, platelet count, symptom duration, gender, and pleural protein levels.
Conclusions:
- AI models can effectively aid in the early diagnosis and prognosis of MPM.
- While biopsy and imaging are accurate, AI offers a potentially more accessible and efficient approach.
- Identifying key clinical predictors enhances the ability to prognosticate Mesothelioma.
More Related Videos
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
07:15Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
