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Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique.

M Shobana1, V R Balasraswathi2, R Radhika2

  • 1SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Kanchipuram, 603203, Chennai, India.

Biomed Research International
|August 8, 2022
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Summary
This summary is machine-generated.

This study introduces a machine learning approach for classifying malignant mesothelioma (MM), a rare and aggressive cancer. Feature selection significantly enhances the accuracy of MM detection models.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Data Science

Background:

  • Malignant mesothelioma (MM) is a rare, aggressive cancer often diagnosed at advanced stages, with limited treatment efficacy.
  • A strong association exists between asbestos exposure and the development of malignant mesothelioma.
  • Current treatments for advanced MM show limited success, highlighting the need for improved diagnostic and classification methods.

Purpose of the Study:

  • To propose a novel classification and detection method for malignant mesothelioma (MM) utilizing machine learning.
  • To enhance the accuracy of MM classification through effective feature selection techniques.

Main Methods:

  • Employed the CFS (correlation-based feature selection) approach for identifying relevant features for MM classification.
  • Utilized Naive Bayes, Fuzzy SVM, and the ID3 algorithm for classifying mesothelioma cancer.
  • Evaluated the performance of machine learning strategies using various metrics.

Main Results:

  • Feature selection using CFS demonstrated a substantial positive impact on the accuracy of the classification models.
  • The chosen machine learning algorithms (Naive Bayes, Fuzzy SVM, ID3) were effective in classifying mesothelioma.
  • The selection of pertinent features was found to be critical for improving classification accuracy.

Conclusions:

  • Machine learning, particularly with effective feature selection, offers a promising avenue for improving malignant mesothelioma classification and detection.
  • The CFS approach is effective in identifying key features that enhance the performance of mesothelioma diagnostic models.
  • Further research into machine learning applications can aid in earlier and more accurate diagnosis of this rare cancer.