International Journal of Computer Techniques Volume 12 Issue 4 | Performance Analysis of Different Machine Learning Algorithm for Detection Of Lung Cancer
Performance Analysis of Different Machine Learning Algorithms for Detection of Lung Cancer
Authors:
Kabita Sahoo, Research Scholar, Biju Patnaik University of Technology, Rourkela, Odisha, India (kabitasahoo789@gmail.com)
Dr. Abhaya Kumar Samal, Professor, Dept. of CSE, Trident Academy of Technology, BPUT, Odisha, India (kabhaya1@gmail.com)
Sekharesh Barik, Assistant Professor, Ajay Binod Institute of Technology
Journal: International Journal of Computer Techniques (IJCT)
Volume: 12 | Issue: 4 | Publication Date: July – August 2025
ISSN: 2394-2231 | Journal URL: https://ijctjournal.org/
Abstract
This paper presents a comparative analysis of ten machine learning algorithms for early detection of lung cancer. Models including Logistic Regression, Decision Tree, KNN, Naïve Bayes (Gaussian and Multinomial), SVM, Random Forest, XGBoost, Gradient Boosting, and MLP were evaluated. Gradient Boosting achieved the highest accuracy, while Multinomial Naïve Bayes performed the lowest. The study emphasizes the importance of early diagnosis and proposes future integration with CT scan-based deep learning models.
Keywords
Lung Cancer, Machine Learning, Early Detection, Gradient Boosting, XGBoost, Naïve Bayes, SVM, CT Scan, Deep Learning, Classification Algorithms
Conclusion
Early detection of lung cancer is critical for improving survival rates. This study demonstrates that ML algorithms can achieve high accuracy in predicting lung cancer using clinical variables. Gradient Boosting emerged as the most effective model. Future work will explore CT scan image integration and deep learning for enhanced diagnostic precision.
References
Includes 14 references from Discover AI, IEEE, Scientific Reports, and other journals covering ML-based lung cancer detection and performance evaluation.
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