An efficient interpretable stacking ensemble model for lung cancer prognosis.

Autor: Arif U; School of Mathematics and Statistics, Xi'an Jiaotong University, Xian, Shaanxi 710049, China. Electronic address: umairrana1765@gmail.com., Zhang C; School of Mathematics and Statistics, Xi'an Jiaotong University, Xian, Shaanxi 710049, China. Electronic address: cxzhang@mail.xjtu.edu.cn., Hussain S; School of Mathematics and Statistics, Xi'an Jiaotong University, Xian, Shaanxi 710049, China. Electronic address: sajid.khoja1000@gmail.com., Abbasi AR; Department of Statistics, COMSATS University Islamabad, Lahore Campus, Lahore 5400, Pakistan. Electronic address: raufabbasi5@gmail.com.
Jazyk: angličtina
Zdroj: Computational biology and chemistry [Comput Biol Chem] 2024 Dec; Vol. 113, pp. 108248. Date of Electronic Publication: 2024 Oct 15.
DOI: 10.1016/j.compbiolchem.2024.108248
Abstrakt: Lung cancer significantly contributes to global cancer mortality, posing challenges in clinical management. Early detection and accurate prognosis are crucial for improving patient outcomes. This study develops an interpretable stacking ensemble model (SEM) for lung cancer prognosis prediction and identifies key risk factors. Using a Kaggle dataset of 1000 patients with 22 variables, the model classifies prognosis into Low, Medium, and High-risk categories. The bootstrap method was employed for evaluation metrics, while SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) assessed model interpretability. Results showed SEM's superior interpretability over traditional models, such as Random Forest, Logistic Regression, Decision Tree, Gradient Boosting Machine, Extreme Gradient Boosting Machine, and Light Gradient Boosting Machine. SEM achieved an accuracy of 98.90 %, precision of 98.70 %, F1 score of 98.85 %, sensitivity of 98.77 %, specificity of 95.45 %, Cohen's kappa value of 94.56 %, and an AUC of 98.10 %. The SEM demonstrated robust performance in lung cancer prognosis, revealing chronic lung cancer and genetic risk as major factors.
Competing Interests: Declaration of Competing Interest All authors declared that they have no conflict of interest.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: