Integrating Advanced Techniques: RFE-SVM Feature Engineering and Nelder-Mead Optimized XGBoost for Accurate Lung Cancer Prediction

Ayad, Sarah; Al-Jamimi, Hamdi A.; El Kheir, Ammar

doi:10.1109/ACCESS.2025.3536034

Article (Scientific journals)

Integrating Advanced Techniques: RFE-SVM Feature Engineering and Nelder-Mead Optimized XGBoost for Accurate Lung Cancer Prediction

Ayad, Sarah; Al-Jamimi, Hamdi A.; El Kheir, Ammar

2025 • In IEEE Access, 13, p. 29589 - 29600

Peer Reviewed verified by ORBi

Permalink
https://hdl.handle.net/2268/333224

DOI
10.1109/ACCESS.2025.3536034

Files (1)Send to Details Statistics Bibliography Similar publications

Files

Full Text

access-ayad-3536034-proof.pdf

Author postprint (2.41 MB)

Creative Commons License - Attribution, Non-Commercial, No Derivative

Download

All documents in ORBi are protected by a user license.

Send to

RIS BibTex APA Chicago Permalink X Linkedin

Details

Keywords :

Early prediction; feature engineering; lung cancer; XGBoost; Cancer prediction; Feature engineerings; Lung Cancer; Medical data sets; Nelder meads; Patient survivals; Recursive feature elimination; Support vectors machine; Xgboost; Computer Science (all); Materials Science (all); Engineering (all)

Abstract :

[en] Early detection of lung cancer is crucial for improving patient survival and reducing mortality. However, medical datasets often face challenges like irrelevant features and class imbalance, complicating accurate predictions. This study presents a comprehensive AI-powered lung cancer classification approach that enhances predictive accuracy and treatment planning. Our methodology combines Recursive Feature Elimination with Support Vector Machines (RFE-SVM) for effective feature selection and employs the XGBoost ensemble learning algorithm for classification, optimized using the Nelder-Mead algorithm. Evaluating the model’s generalizability on two distinct lung cancer datasets, results show that our approach outperforms traditional machine learning models, achieving 100% accuracy. This research highlights the importance of advanced computational techniques in healthcare, paving the way for more personalized and effective patient care.

Disciplines :

Laboratory medicine & medical technology

Author, co-author :

Ayad, Sarah ; Arab Open University, Faculty of Computer Studies, Riyadh, Saudi Arabia

Al-Jamimi, Hamdi A. ; King Fahd University of Petroleum Minerals, Information and Computer Science, Dhahran, Saudi Arabia

El Kheir, Ammar ; Université de Liège - ULiège > Département des sciences cliniques > Pédiatrie

Language :

English

Title :

Integrating Advanced Techniques: RFE-SVM Feature Engineering and Nelder-Mead Optimized XGBoost for Accurate Lung Cancer Prediction

Publication date :

2025

Journal title :

IEEE Access

ISSN :

2169-3536

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Volume :

Pages :

29589 - 29600

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://xplorestaging.ieee.org/ielx8/6287639/10820123/10857335.pdf?arnumber=10857335

Funders :

AOU - Arab Open University
KFUPM - King Fahd University of Petroleum and Minerals

Funding text :

This work was supported by Arab Open University under Grant AOUKSA-524008. The work of Hamdi A. Al-Jamimi was supported by the King Fahd University of Petroleum and Minerals (KFUPM). The authors extend their appreciation to the Arab Open University for funding this work through research fund No. AOUKSA-524008. The first author would like to acknowledge the help and support provided by King Fahd University of Petroleum and Minerals (KFUPM).

Available on ORBi :

since 17 June 2025

Statistics

Number of views

45 (0 by ULiège)

Number of downloads

26 (0 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, ‘‘Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,’’ CA, A Cancer J. Clinicians, vol. 71, no. 3, pp. 209–249, May 2021.
L. A. Torre, R. L. Siegel, and A. Jemal, ‘‘Lung cancer statistics,’’ in Lung Cancer and Personalized Medicine: Current Knowledge and Therapies. Cham, Switzerland: Springer, 2016, pp. 1–19.
H. Lemjabbar-Alaoui, O. U. Hassan, Y.-W. Yang, and P. Buchanan, ‘‘Lung cancer: Biology and treatment options,’’ Biochimica et Biophysica Acta (BBA)-Rev. Cancer, vol. 1856, no. 2, pp. 189–210, Dec. 2015.
C. R. Thomas Jr., Lung Cancer: A Multidisciplinary Approach to Diagnosis and Management. New York, NY, USA: Demos Medical Publishing, 2010.
C. Ladbury, A. Amini, A. Govindarajan, I. Mambetsariev, D. J. Raz, E. Massarelli, T. Williams, A. Rodin, and R. Salgia, ‘‘Integration of artificial intelligence in lung cancer: Rise of the machine,’’ Cell Rep. Med., vol. 4, no. 2, Feb. 2023, Art. no. 100933.
S. M. Varnosfaderani and M. Forouzanfar, ‘‘The role of AI in hospitals and clinics: Transforming healthcare in the 21st century,’’ Bioengineering, vol. 11, no. 4, p. 337, Mar. 2024.
G. Chassagnon, C. D. Margerie-Mellon, M. Vakalopoulou, R. Marini, T.-N. Hoang-Thi, M.-P. Revel, and P. Soyer, ‘‘Artificial intelligence in lung cancer: Current applications and perspectives,’’ Jpn. J. Radiol., vol. 41, pp. 235–244, Nov. 2022.
A. P. Zhao, S. Li, Z. Cao, P. J.-H. Hu, J. Wang, Y. Xiang, D. Xie, and X. Lu, ‘‘AI for science: Predicting infectious diseases,’’ J. Saf. Sci. Resilience, vol. 5, no. 2, pp. 130–146, Jun. 2024.
H. A. Al-Jamimi, ‘‘Synergistic feature engineering and ensemble learning for early chronic disease prediction,’’ IEEE Access, vol. 12, pp. 62215–62233, 2024.
Ş. Ay, E. Ekinci, and Z. Garip, ‘‘A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases,’’ J. Supercomput., vol. 79, no. 11, pp. 11797–11826, Jul. 2023.
S. Zhao, P. Wang, A. A. Heidari, H. Chen, W. He, and S. Xu, ‘‘Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy,’’ Comput. Biol. Med., vol. 139, Dec. 2021, Art. no. 105015.
H.-Y. Chiu, H.-S. Chao, and Y.-M. Chen, ‘‘Application of artificial intelligence in lung cancer,’’ Cancers, vol. 14, no. 6, p. 1370, Mar. 2022.
S. Huang, J. Yang, N. Shen, Q. Xu, and Q. Zhao, ‘‘Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective,’’ in Seminars in Cancer Biology, vol. 89. Amsterdam, The Netherlands: Elsevier, 2023, pp. 30–37.
M. Liu, J. Wu, N. Wang, X. Zhang, Y. Bai, J. Guo, L. Zhang, S. Liu, and K. Tao, ‘‘The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis,’’ PLoS ONE, vol. 18, no. 3, Mar. 2023, Art. no. e0273445.
X.-W. Chen and J. C. Jeong, ‘‘Enhanced recursive feature elimination,’’ in Proc. 6th Int. Conf. Mach. Learn. Appl. (ICMLA), Dec. 2007, pp. 429–435.
M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, ‘‘Support vector machines,’’ IEEE Intell. Syst. Appl., vol. 13, no. 4, pp. 18–28, Apr. 1998.
W. S. Noble, ‘‘What is a support vector machine?’’ Nature Biotechnol., vol. 24, no. 12, pp. 1565–1567, Dec. 2006.
X. Zhou, Y. Chen, Z. Wu, A. A. Heidari, H. Chen, E. Alabdulkreem, J. Escorcia-Gutierrez, and X. Wang, ‘‘Boosted local dimensional mutation and all-dimensional neighborhood slime mould algorithm for feature selection,’’ Neurocomputing, vol. 551, Sep. 2023, Art. no. 126467.
F. Gao and L. Han, ‘‘Implementing the Nelder–Mead simplex algorithm with adaptive parameters,’’ Comput. Optim. Appl., vol. 51, no. 1, pp. 259–277, Jan. 2012.
C. De Margerie-Mellon and G. Chassagnon, ‘‘Artificial intelligence: A critical review of applications for lung nodule and lung cancer,’’ Diagnostic Interventional Imag., vol. 104, no. 1, pp. 11–17, Jan. 2023.
H. Park, J. Yun, S. M. Lee, H. J. Hwang, J. B. Seo, Y. J. Jung, J. Hwang, S. H. Lee, S. W. Lee, and N. Kim, ‘‘Deep learning—Based approach to predict pulmonary function at chest CT,’’ Radiology, vol. 307, no. 2, Apr. 2023, Art. no. 221488.
T. I. A. Mohamed and A. E.-S. Ezugwu, ‘‘Enhancing lung cancer classification and prediction with deep learning and multi-omics data,’’ IEEE Access, vol. 12, pp. 59880–59892, 2024.
S. K. Bhatt and S. Srinivasan, ‘‘Lung cancer detection using ai and different techniques of machine learning,’’ Int. J. Intell. Syst. Appl. Eng., vol. 12, no. 8, pp. 630–638, 2024.
P. Sathe, A. Mahajan, D. Patkar, and M. Verma, ‘‘End-to-end fully automated lung cancer screening system,’’ IEEE Access, vol. 12, pp. 108515–108532, 2024.
A. Heidari, D. Javaheri, S. Toumaj, N. J. Navimipour, M. Rezaei, and M. Unal, ‘‘A new lung cancer detection method based on the chest CT images using federated learning and blockchain systems,’’ Artif. Intell. Med., vol. 141, Jul. 2023, Art. no. 102572.
M. G. Lanjewar, K. G. Panchbhai, and P. Charanarur, ‘‘Lung cancer detection from CT scans using modified DenseNet with feature selection methods and ML classifiers,’’ Expert Syst. Appl., vol. 224, Aug. 2023, Art. no. 119961.
M. Mamun, M. I. Mahmud, M. Meherin, and A. Abdelgawad, ‘‘LCDctCNN: Lung cancer diagnosis of CT scan images using CNN based model,’’ in Proc. 10th Int. Conf. Signal Process. Integr. Netw. (SPIN), Mar. 2023, pp. 205–212.
S. Wankhade and S. Vigneshwari, ‘‘A novel hybrid deep learning method for early detection of lung cancer using neural networks,’’ Healthcare Analytics, vol. 3, Nov. 2023, Art. no. 100195.
A. Seth and V. D. Kaushik, ‘‘Lung and colon classification using improved local Fisher discriminant analysis with ANFIS,’’ Int. J. Inf. Technol., vol. 16, no. 8, pp. 4845–4853, Dec. 2024.
B. Mostafa, M. Sakr, and A. Keshk, ‘‘Employing the capabilities of LSTM and Bi-LSTM for lung cancer detection and classification,’’ Int. J. Intell. Eng. Syst., vol. 17, no. 5, pp. 412–423, 2024.
K. Priyadarshini, M. Alagarsamy, K. Sangeetha, and D. Thangaraju, ‘‘Hybrid RNN-FFBPNN optimized with glowworm swarm algorithm for lung cancer prediction,’’ IETE J. Res., vol. 70, no. 5, pp. 4453–4468, May 2024.
N. A. Wani, R. Kumar, and J. Bedi, ‘‘DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence,’’ Comput. Methods Programs Biomed., vol. 243, Jan. 2024, Art. no. 107879.
S. T. Rikta, K. M. M. Uddin, N. Biswas, R. Mostafiz, F. Sharmin, and S. K. Dey, ‘‘XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancer,’’ J. Pathol. Informat., vol. 14, Apr. 2023, Art. no. 100307.
Y. Li, X. Wu, P. Yang, G. Jiang, and Y. Luo, ‘‘Machine learning for lung cancer diagnosis, treatment, and prognosis,’’ Genomics, Proteomics Bioinf., vol. 20, no. 5, pp. 850–866, Oct. 2022.
K. Jabir and A. Thirumurthi Raja, ‘‘Prediction of lung cancer from electronic health records using CNN supported nlp,’’ in Computational Intelligence for Clinical Diagnosis. Cham, Switzerland: Springer, 2023, pp. 549–560.
I. M. Nasser and S. S. Abu-Naser, ‘‘Lung cancer detection using artificial neural network,’’ Int. J. Eng. Inf. Syst., vol. 3, pp. 17–23, Mar. 2019.
E. Dritsas and M. Trigka, ‘‘Lung cancer risk prediction with machine learning models,’’ Big Data Cognit. Comput., vol. 6, no. 4, p. 139, Nov. 2022.
M. Mamun, A. Farjana, M. A. Mamun, and M. S. Ahammed, ‘‘Lung cancer prediction model using ensemble learning techniques and a systematic review analysis,’’ in Proc. IEEE World AI IoT Congr. (AIIoT), Jun. 2022, pp. 187–193.
(2024). Lung Cancer Risk Dataset. [Online]. Available: https://www.kaggle.com/datasets/nancyalaswad90/lung-cancer
(2024). Lung Cancer Patient Dataset. [Online]. Available: https://www.kaggle.com/datasets/thedevastator/cancer-patients-and-air-pollution-anew-link
Pamir, N. Javaid, A. Almogren, M. Adil, M. U. Javed, and M. Zuair, ‘‘RFE based feature selection and KNNOR based data balancing for electricity theft detection using BiLSTM-LogitBoost stacking ensemble model,’’ IEEE Access, vol. 10, pp. 112948–112963, 2022.
P. Theerthagiri and S. D. Siddalingaiah, ‘‘RG-SVM: Recursive Gaussian support vector machine based feature selection algorithm for liver disease classification,’’ Multimedia Tools Appl., vol. 83, no. 20, pp. 59021–59042, Dec. 2023.
T. Chen, ‘‘XGBoost: Extreme gradient boosting,’’ R Package Version 0.4-2, vol. 1, no. 4, 2015.
P. Zhang, Y. Jia, and Y. Shang, ‘‘Research and application of XGBoost in imbalanced data,’’ Int. J. Distrib. Sensor Netw., vol. 18, no. 6, Jun. 2022, Art. no. 155013292211069.
T.-T.-H. Le, Y. E. Oktian, and H. Kim, ‘‘XGBoost for imbalanced multiclass classification-based industrial Internet of Things intrusion detection systems,’’ Sustainability, vol. 14, no. 14, p. 8707, Jul. 2022.
A. Ogunleye and Q.-G. Wang, ‘‘XGBoost model for chronic kidney disease diagnosis,’’ IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 17, no. 6, pp. 2131–2140, Nov. 2020.
A. Paleczek, D. Grochala, and A. Rydosz, ‘‘Artificial breath classification using XGBoost algorithm for diabetes detection,’’ Sensors, vol. 21, no. 12, p. 4187, Jun. 2021.
V. Jain and M. Agrawal, ‘‘Heart failure prediction using XGB classifier, logistic regression and support vector classifier,’’ in Proc. Int. Conf. Advancement Comput. Comput. Technol. (InCACCT), May 2023, pp. 1–5.
L. Yang and A. Shami, ‘‘On hyperparameter optimization of machine learning algorithms: Theory and practice,’’ Neurocomputing, vol. 415, pp. 295–316, Nov. 2020.
E. Kusuma, G. Shidik, and R. Pramunendar, ‘‘Optimization of neural network using Nelder Mead in breast cancer classification,’’ Int. J. Intell. Eng. Syst., vol. 13, no. 6, pp. 330–337, Dec. 2020.
M. A. Luersen and R. L. Riche, ‘‘Globalized Nelder–Mead method for engineering optimization,’’ Comput. Struct., vol. 82, no. 26, pp. 2251–2260, Aug. 2004.