Detection of lung cancer mutation based on clinical and morphological features using adaptive boosting method

Abstract

Lung cancer is a leading cause of cancer-related mortality worldwide, and accurate detection of epidermal growth factor receptor mutations is essential for personalized treatment. However, non-invasive identification of these mutations remains challenging due to the complexity of clinical and morphological patterns. This study develops an adaptive boosting (AdaBoost)-based machine learning model for detecting lung cancer mutations using clinical and morphological data. The dataset consists of clinical and morphological attributes from 80 patients, which processed through comprehensive preprocessing steps, including imputation, outlier removal, and feature selection. One-hot encoding increased the feature count beyond the original 28, and analysis of variance was employed to retain the most relevant 33 features. AdaBoost was trained with optimized hyperparameters, including learning rate and the number of estimators, which were tuned using grid search to ensure robustness. The model’s performance was evaluated using an 80/20 train-test split and k-fold cross-validation to assess generalization capability. Experimental results demonstrated that AdaBoost outperformed other models, achieving an accuracy of 83% and an area under the curve of 0.90 after feature selection. The model maintained superior cross-validation scores compared to Naive Bayes, decision tree, K-nearest neighbors, and support vector machine, reinforcing its reliability in mutation detection. The study highlights the significance of preprocessing steps in improving classification performance and suggests that AdaBoost can serve as an effective, non-invasive tool for assisting clinical decision-making in lung cancer mutation detection.

Keywords

Adaptive Boosting / Analysis of Variance / Lung Cancer / Machine Learning / Mutation

References

[1] Benhar H., Idri A., & Fernández-Alemán J.L. (2020). Data preprocessing for heart disease classification: A systematic literature review. Computer Methods and Programs in Biomedicine, 195, 105635. https://doi.org/10.1016/j.cmpb.2020.105635

[2] Berger A., & Kiefer M. (2021). Comparison of different response time outlier exclusion methods: A simulation study. Frontiers in Psychology, 12, 675558. https://doi.org/10.3389/fpsyg.2021.675558

[3] Bushara A.R., Vinod Kumar R.S., & Kumar S.S. (2023). An ensemble method for the detection and classification of lung cancer using computed tomography images utilizing a capsule network with visual geometry group. Biomedical Signal Processing and Control, 85, 104930. https://doi.org/10.1016/j.bspc.2023.104930

[4] Gautam N., Basu A., & Sarkar R. (2024). Lung cancer detection from thoracic CT scans using an ensemble of deep learning models. Neural Computing and Applications, 36(5), 2459-2477. https://doi.org/10.1007/s00521-023-09130-7

[5] González S., García S., Del Ser J., Rokach L., & Herrera F. (2020). A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities. Information Fusion, 64, 205-237. https://doi.org/10.1016/j.inffus.2020.07.007

[6] Jain R., Singh P., Abdelkader M., & Boulila W. (2024). Efficient lung cancer detection using computational intelligence and ensemble learning. PLOS ONE, 19(9), e0310882. https://doi.org/10.1371/journal.pone.0310882

[7] Kanan M., Alharbi H., Alotaibi N., Almasuood L., Aljoaid S., Alharbi T., et al. (2024). AI-driven models for diagnosing and predicting outcomes in lung cancer: A systematic review and meta-analysis. Cancers (Basel), 16(3), 674. https://doi.org/10.3390/cancers16030674

[8] Kwon H.J., Park U.H., Goh C.J., Park D., Lim Y.G., Lee I.K., et al. (2023). Enhancing lung cancer classification through integration of liquid biopsy multi-omics data with machine learning techniques. Cancers (Basel), 15(18), 4556. https://doi.org/10.3390/cancers15184556

[9] Le N.Q.K., Kha Q.H., Nguyen V.H., Chen Y.C., Cheng S.J., & Chen C.Y. (2021). Machine learning-based radiomics signatures for EGFR and KRAS mutations prediction in non-small-cell lung cancer. International Journal of Molecular Sciences, 22(17), 9254. https://doi.org/10.3390/ijms22179254

[10] Li X. (2023). Lung cancer risk prediction and feature importance analysis with machine learning algorithm. Applied and Computational Engineering, 19, 205-210. https://doi.org/10.54254/2755-2721/19/20231034

[11] Maurya S.P., Sisodia P.S., Mishra R., & Singh D.P. (2024). Performance of machine learning algorithms for lung cancer prediction: A comparative approach. Scientific Reports, 14(1), 18562. https://doi.org/10.1038/s41598-024-58345-8

[12] Rakesh M., & Baskar R. (2024). A support vector machine for lung cancer detection with classification and compared with KNN for better accuracy. AIP Conference Proceedings, 2853(1), 020067. https://doi.org/10.1063/5.0198176

[13] Rincy T.N., & Gupta R. (2020). Ensemble Learning Techniques and its Efficiency in Machine Learning:A Survey. 2nd International Conference on Data, Engineering and Applications (IDEA). p1-6. https://doi.org/10.1109/IDEA49133.2020.9170675

[14] Sachdeva R.K., Bathla P., Rani P., Lamba R., Ghantasala G.S.P., & Nassar I.F. (2024). A novel K-nearest neighbor classifier for lung cancer disease diagnosis. Neural Computing and Applications. 36, 22403-22416. https://doi.org/10.1007/s00521-024-10235-w

[15] Wang S., Shi J., Ye Z., Dong D., Yu D., Zhou M., et al. (2019). Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. European Respiratory Journal. 53, 1800986. https://doi.org/10.1183/13993003.00986-2018

[16] Yu L., Tao G., Zhu L., Wang G., Li Z., Ye, J., et al. (2019). Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis. BMC Cancer, 19(1), 464. https://doi.org/10.1186/s12885-019-5646-9

[17] Zhou Z.H. (2012). Ensemble Methods: Foundations and Algorithms. 1st ed. Chapman and Hall/CRC, Boca Raton.