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A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis.

Abdulla Mousa Falah Alali1, Dhyaram Lakshmi Padmaja2, Mukesh Soni3

  • 1Department of Computer Science, Isra University, Amman, Jordan.

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|November 13, 2023
PubMed
Summary

This study highlights the effectiveness of machine learning for diagnosing malignant mesothelioma (MM), a type of lung cancer. Support vector machine (SVM) achieved 99.87% accuracy, outperforming neural networks for MM detection.

Keywords:
lung cancermagnetic resonance imagingmalignant mesotheliomassupport vector machine

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Area of Science:

  • Oncology
  • Medical Informatics
  • Data Science

Background:

  • Malignant mesothelioma (MM) is a significant cause of cancer mortality globally.
  • Early diagnosis of MM is challenging due to asymptomatic presentation and the limitations of current detection methods.
  • Identifying MM risk factors and improving diagnostic accuracy are critical for patient outcomes.

Purpose of the Study:

  • To investigate the utility of data mining and classification algorithms for diagnosing malignant mesothelioma.
  • To compare the performance of Support Vector Machine (SVM) against Multilayer Perceptron Ensembles (MLPE) neural network (NN) for MM classification.
  • To identify efficient computational methods for MM diagnosis using patient data.

Main Methods:

  • Utilized a dataset containing information from both mesothelioma patients and healthy individuals.
  • Applied computationally efficient data mining techniques, specifically classification algorithms.
  • Employed 10-fold cross-validation over 5 runs to evaluate model performance.
  • Conducted SPSS analysis for data collection and experimental validation.

Main Results:

  • Support Vector Machine (SVM) demonstrated superior performance in classifying MM.
  • SVM achieved a classification accuracy of 99.87%.
  • MLPE neural network (NN) achieved a classification accuracy of 99.56%, which was lower than SVM.

Conclusions:

  • SVM is a highly effective classification method for diagnosing malignant mesothelioma.
  • Machine learning, particularly SVM, offers a promising and accurate approach for MM detection.
  • These findings suggest a potential for improved, data-driven diagnostic tools for lung cancer subtypes.