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Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure.

Komal Saxena1, Abu Sarwar Zamani2, R Bhavani3

  • 1Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India.

Biomed Research International
|July 18, 2022
PubMed
Summary
This summary is machine-generated.

This study evaluated artificial intelligence (AI) methods for mesothelioma detection. Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LogR), and Random Forest (RF) achieved 100% accuracy, outperforming other AI approaches.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Mesothelioma is an aggressive cancer affecting organs like the lungs, heart, and stomach.
  • Accurate and early detection of mesothelioma is crucial for patient outcomes.
  • Various machine learning algorithms are explored for their potential in clinical decision support systems.

Purpose of the Study:

  • To investigate and compare the performance of multiple artificial intelligence (AI) methodologies for mesothelioma detection.
  • To evaluate the diagnostic accuracy and computational complexity of different AI models in identifying mesothelioma.
  • To determine the most effective AI approaches for mesothelioma diagnosis based on precision and efficiency.

Main Methods:

  • Utilized a dataset comprising 350 instances with 35 features for evaluating AI models.
  • Applied several machine learning algorithms: K-nearest neighbors (KNN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LogR).
  • Assessed model performance using six execution measures, focusing on diagnostic precision and computational complexity.

Main Results:

  • Linear Discriminant Analysis (LDA) achieved 65% precision, Naive Bayes (NB) 70%, and K-nearest neighbors (KNN) 92%.
  • Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LogR), and Random Forest (RF) all demonstrated 100% precision.
  • The computational complexity of each approach was also evaluated, with SVM, DT, LogR, and RF showing superior performance.

Conclusions:

  • Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LogR), and Random Forest (RF) are highly effective AI methods for mesothelioma detection.
  • These top-performing AI models significantly outperform previous research in accuracy and efficiency.
  • The findings suggest advanced AI techniques can enhance clinical decision support systems for diagnosing challenging cancers like mesothelioma.