The mesothelioma disease diagnosis with artificial intelligence methods

İlhan, Hamza Osman; Çelik, Enes

doi:10.1109/icaict.2016.7991825

Cited by 19 publications

(9 citation statements)

References 9 publications

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“…The investigation carried by Ilhan and Celik 28 used Ensemble models to achieve 100% accurateness score to diagnose mesothelioma malignant cases. Another study conceded by Kaur and Singh 29 deployed KNN to classify between malignant and benign cases.…”

Section: Related Workmentioning

confidence: 99%

A comprehensive data‐level investigation of cancer diagnosis on imbalanced data

Gupta

2021

Computational Intelligence

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Cancer is one of the leading causes of death in the world. Cancer research is vital as the prognosis of cancer enables clinical applications for patients. In this study, we have proposed the Stacked Ensemble Model (Stacking of bagged and boosted learners) for the automatic disease diagnosis. The experimental results prove the superiority of the proposed method to conventional machine learning techniques. In the empirical study, the performance of eight data handling methods and 14 classification methods is compared to obtain prediction results. The performance of the model has been evaluated on five benchmark datasets. The appreciable Area under the Curve scores achieved by the proposed methodology on Cervical Cancer (0.98), Mesothelioma (0.93), Breast Cancer (0.99), Prostate Cancer (0.97), and Hepatitis-C Virus (0.998) datasets make this work more significant than the previously published works. The experimental results show that our proposed method is superior to conventional machine learning techniques and the proposed model contributes in the form of an efficient computational model.

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Section: Related Workmentioning

confidence: 99%

A comprehensive data‐level investigation of cancer diagnosis on imbalanced data

Gupta

2021

Computational Intelligence

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“…The common symptoms of the disease can be found in human body as constant pain and progressive shortness of breath can lead the human to expire in a short interval. In [101], Support Vector Machine (SVM), Neural Network (NN) and Decision Tree (DT) are selected as Regular ML techniques; while Bagging and Adaboost are selected as Ensemble ML techniques. Authors used 324 data samples having 34 features in the study.…”

Section: Literature Reviewmentioning

confidence: 99%

“…On the other hand, DT fails on handling big data problem. In the conclusion, linear SVM is considered as the best algorithm to diagnose Malignant Mesothelioma disease [101].…”

Section: Literature Reviewmentioning

confidence: 99%

Taxonomy on Healthcare System Based on Machine Learning Approaches: Tuberculosis Disease Diagnosis

Karmani¹,

Chandio²,

Karmani³

et al. 2020

IJCDS

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“…There exists some work of literature that has used artificial intelligence or machine learning algorithms such as decision tree, random forest, support vector machine, and even artificial neural network to identify MM (Choudhury , Identification of Cancer: Mesothelioma's Disease Using Logistic Regression and Association Rule, 2018) (Ilhan & Celik, 2016) but with some limitations.…”

Section: Problem Statementmentioning

confidence: 99%

Prognosticating Mesothelioma Using Predictive Analytics

Choudhury¹

2019

Preprint

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Background: Malignant pleural mesothelioma (MPM) is an atypical, belligerent tumor that matures into cancer in the pleura, a stratum of tissue bordering the lungs. Pleural mesothelioma is a common type of mesothelioma that accounts for about 75 percent of all mesothelioma diagnosed yearly in the United States. Diagnosis of mesothelioma takes several months and is expensive. Given the difficulty of diagnosing MPM, early identification is crucial for patient survival. Our study implements artificial intelligence and recommends the best fit model for early diagnosis and prognosis of MPM. Method: We retrospectively retrieved patient’s medical reports generated by Dicle University, Turkey and implemented multi-layered 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 generates 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 imagining tests are strong predictors of mesothelioma but are associated with high cost, however, can identify mesothelioma with optimal accuracy. Predictive analytics without using biopsy results can diagnose mesothelioma with acceptable accuracy. Implementation of phase-2 followed by phase-1 can address diagnosis expenses and maximize disease prognosis. Additionally, results indicate improved MPM diagnosis using AI methods dependent upon the specific application.

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The mesothelioma disease diagnosis with artificial intelligence methods

Cited by 19 publications

References 9 publications

A comprehensive data‐level investigation of cancer diagnosis on imbalanced data

A comprehensive data‐level investigation of cancer diagnosis on imbalanced data

Taxonomy on Healthcare System Based on Machine Learning Approaches: Tuberculosis Disease Diagnosis

Prognosticating Mesothelioma Using Predictive Analytics

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