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Updated: May 5, 2026

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Optimized Machine Learning Pipeline for Lung Cancer Classification: Feature Reduction and Hyperparameter Tuning.

Gufran Ahmad Ansari1, Salliah Shafi2, Lamees Alhazzaa1

  • 1College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|May 4, 2026
PubMed
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An optimized machine learning (ML) framework using routine clinical data improved lung cancer classification accuracy. Simple, well-tuned models like Logistic Regression showed superior performance for early risk screening.

Area of Science:

  • Medical Informatics
  • Computational Biology
  • Machine Learning

Background:

  • Lung cancer is a leading cause of cancer mortality globally, often due to late diagnosis.
  • Existing machine learning (ML) applications for lung cancer classification frequently lack optimized end-to-end pipelines with routine clinical data.
  • This study addresses the need for an improved ML framework integrating diverse patient data for better classification.

Purpose of the Study:

  • To develop and evaluate an optimized machine learning framework for lung cancer classification using routine clinical data.
  • To systematically tune hyperparameters and employ feature selection to enhance classification performance.
  • To assess the effectiveness of various ML classifiers within the proposed framework.

Main Methods:

Keywords:
feature selectionhyperparameter tuninglung cancer classificationmachine learningmetaheuristic optimization

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  • A dataset of 309 patient records with demographic, lifestyle, and clinical features was utilized.
  • Metaheuristic algorithms (Red Deer, Grasshopper, Gray Wolf, Bee Colony Optimization) were employed for feature selection.
  • Six ML classifiers (Logistic Regression, SVM, Gradient Boosting, Random Forest, KNN, GNB) were trained and evaluated using optimized hyperparameters and standard metrics (accuracy, precision, recall, F1, ROC-AUC).

Main Results:

  • The optimized ML pipeline demonstrated significant improvements in classification performance.
  • Logistic Regression achieved the highest accuracy (91.07%) and an AUC of 0.91, surpassing more complex models.
  • Gradient Boosting and Random Forest achieved 87.5% accuracy, with other classifiers showing moderate results.

Conclusions:

  • The proposed optimized ML pipeline effectively enhances lung cancer classification accuracy using routine clinical data.
  • Well-optimized, simpler models can outperform complex ensemble methods on structured datasets.
  • The framework shows promise for early lung cancer risk screening and clinical decision support, warranting further validation on larger cohorts.