XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancer

Sarreha Tasmin Rikta; Khandaker Mohammad Mohi Uddin; Nitish Biswas; Rafid Mostafiz; Fateha Sharmin; Samrat Kumar Dey

doi:10.1016/j.jpi.2023.100307

XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancer

J Pathol Inform. 2023 Mar 24:14:100307. doi: 10.1016/j.jpi.2023.100307. eCollection 2023.

Authors

Sarreha Tasmin Rikta¹, Khandaker Mohammad Mohi Uddin¹, Nitish Biswas¹, Rafid Mostafiz², Fateha Sharmin³, Samrat Kumar Dey⁴

Affiliations

¹ Department of Computer Science and Engineering, Dhaka International University, Dhaka 1205, Bangladesh.
² Institute of Information Technology, Noakhali Science and Technology University, Noakhali, Bangladesh.
³ Department of chemistry, University of Chittagong, Chittagong, Bangladesh.
⁴ School of Science and Technology, Bangladesh Open University, Gazipur 1705, Bangladesh.

Abstract

Lung cancer has been the leading cause of cancer-related deaths worldwide. Early detection and diagnosis of lung cancer can greatly improve the chances of survival for patients. Machine learning has been increasingly used in the medical sector for the detection of lung cancer, but the lack of interpretability of these models remains a significant challenge. Explainable machine learning (XML) is a new approach that aims to provide transparency and interpretability for machine learning models. The entire experiment has been performed in the lung cancer dataset obtained from Kaggle. The outcome of the predictive model with ROS (Random Oversampling) class balancing technique is used to comprehend the most relevant clinical features that contributed to the prediction of lung cancer using a machine learning explainable technique termed SHAP (SHapley Additive exPlanation). The results show the robustness of GBM's capacity to detect lung cancer, with 98.76% accuracy, 98.79% precision, 98.76% recall, 98.76% F-Measure, and 0.16% error rate, respectively. Finally, a mobile app is developed incorporating the best model to show the efficacy of our approach.

Keywords: Explainable machine learning; GBM; Lung cancer; Mobile app; ROS; SHAP.