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Predicting cancer using supervised machine learning: Mesothelioma.

Avishek Choudhury

    Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
    |June 23, 2020
    PubMed
    Summary
    This summary is machine-generated.

    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.

    Keywords:
    Mesotheliomaartificial intelligencedecision support systemlung cancermachine learningpredictive modeling

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    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.