Suitable Supervised Machine Learning Techniques for Malignant Mesothelioma Diagnosis

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dc.contributor.authorWin, Khin Yadanar
dc.contributor.authorManeerat, Noppadol
dc.contributor.authorChoomchuay, Somsak
dc.contributor.authorSreng, Syna
dc.contributor.authorHamamoto, Kazuhiko
dc.date.accessioned2026-08-06T10:23:55Z
dc.date.available2026-08-06T10:23:55Z
dc.date.issued2019-01-10
dc.description.abstractMalignant Mesothelioma (MM) is a rare, aggressive cancer that grows in the lining of the internal organs such as lung, abdomen or heart. Fousing on MM diagnosis, in this paper, we investigate multiple machine learning methods and compare for accurate MM diagnosis results. Seven machine learning algorithms namely (i) Linear Discriminant Analysis (LDA), (ii) Naïve Bayes, (iii) K Nearest Neighborhood (KNN), (iv) Support Vector Machine (SVM), (v) Decision Tree (DT), (vi) Logistic Regression (LogR) and (vii) Random forest (RF) algorithms are exploited. The experiments dataset containing 324 cases with 34 features and six performance measures are used to assess the accuracy of evaluated classifiers. The average accuracy of LDA, NB, KNN, SVM, DT, LogR and RF are 61.73%, 67.90%, 91.36%, 100%, 100%, 100% and 100%, respectively. In addition, the computational complexity of each method is also analyzed. Each algoritm is judged based on its classification accuracy and computational complexity. It is found that SVM, DT, LogR and RF outperform the others and even previous studies.
dc.identifier.citationBmeicon 2018 11th Biomedical Engineering International Conference, 2019
dc.identifier.doi10.1109/BMEiCON.2018.8609935
dc.identifier.other2-s2.0-85062076210
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/9687
dc.sourceBmeicon 2018 11th Biomedical Engineering International Conference
dc.subjectAsbestos exposure
dc.subjectClinical decision support system
dc.subjectMachine learning
dc.subjectMalignant mesotheolioma
dc.titleSuitable Supervised Machine Learning Techniques for Malignant Mesothelioma Diagnosis
dc.typeConference Paper

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