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Optimizing lung cancer classification through hyperparameter tuning.

Syed Muhammad Nabeel1, Sibghat Ullah Bazai1, Nada Alasbali2

  • 1Department of Computer Engineering, Balochistan University of Information Technology, Engineering, and Management Sciences (BUITEMS), Quetta, Balochistan, Pakistan.

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Summary

This study introduces a novel machine learning approach for precise lung cancer detection. The developed method achieved 99.16% accuracy, offering a less invasive and cost-effective diagnostic tool.

Keywords:
Lung cancerXGBoostand logistic regressiondecision treeshyperparameter tuningmachine learningsupport vector machines

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Area of Science:

  • Medical Informatics
  • Oncology
  • Artificial Intelligence

Background:

  • Lung cancer is a leading cause of mortality globally, necessitating improved diagnostic methods.
  • Current diagnostic techniques can be invasive and costly.
  • Artificial intelligence (AI) offers potential for enhancing healthcare diagnostics.

Purpose of the Study:

  • To develop a machine learning (ML) strategy for precise, less invasive, and cost-effective lung cancer detection.
  • To evaluate the performance of novel ML methods against existing techniques.

Main Methods:

  • Proposed and benchmarked four ML methods for lung cancer detection.
  • Utilized a recognized Kaggle dataset for evaluation.
  • Employed hyperparameter tuning, specifically optimizing Gamma and C parameters to 10 for the most promising method.

Main Results:

  • One proposed ML method significantly outperformed existing techniques.
  • Achieved high performance metrics: 99.16% accuracy, 98% precision, and 100% sensitivity.
  • Hyperparameter tuning of Gamma and C parameters was crucial for optimal performance.

Conclusions:

  • The enhanced ML prediction mechanism surpasses traditional and contemporary lung cancer detection strategies.
  • The developed method offers a promising advancement in early and accurate lung cancer diagnosis.
  • This AI-driven approach has the potential to improve patient outcomes through more effective screening.