XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancer
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 interp...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353923001219 |
| _version_ | 1797855291185823744 |
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| author | Sarreha Tasmin Rikta Khandaker Mohammad Mohi Uddin Nitish Biswas Rafid Mostafiz Fateha Sharmin Samrat Kumar Dey |
| author_facet | Sarreha Tasmin Rikta Khandaker Mohammad Mohi Uddin Nitish Biswas Rafid Mostafiz Fateha Sharmin Samrat Kumar Dey |
| author_sort | Sarreha Tasmin Rikta |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T20:21:21Z |
| format | Article |
| id | doaj.art-d6b06d278d7a4bf0bf6af00f9ce8acf7 |
| institution | Directory Open Access Journal |
| issn | 2153-3539 |
| language | English |
| last_indexed | 2024-04-09T20:21:21Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-d6b06d278d7a4bf0bf6af00f9ce8acf72023-03-31T05:53:11ZengElsevierJournal of Pathology Informatics2153-35392023-01-0114100307XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancerSarreha Tasmin Rikta0Khandaker Mohammad Mohi Uddin1Nitish Biswas2Rafid Mostafiz3Fateha Sharmin4Samrat Kumar Dey5Department of Computer Science and Engineering, Dhaka International University, Dhaka 1205, BangladeshDepartment of Computer Science and Engineering, Dhaka International University, Dhaka 1205, Bangladesh; Corresponding author at: Department of Computer Science and Engineering, Dhaka International University, Dhaka 1205, Bangladesh.Department of Computer Science and Engineering, Dhaka International University, Dhaka 1205, BangladeshInstitute of Information Technology, Noakhali Science and Technology University, Noakhali, BangladeshDepartment of chemistry, University of Chittagong, Chittagong, BangladeshSchool of Science and Technology, Bangladesh Open University, Gazipur 1705, BangladeshLung 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.http://www.sciencedirect.com/science/article/pii/S2153353923001219Lung cancerExplainable machine learningROSSHAPGBMMobile app |
| spellingShingle | Sarreha Tasmin Rikta Khandaker Mohammad Mohi Uddin Nitish Biswas Rafid Mostafiz Fateha Sharmin Samrat Kumar Dey XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancer Journal of Pathology Informatics Lung cancer Explainable machine learning ROS SHAP GBM Mobile app |
| title | XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancer |
| title_full | XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancer |
| title_fullStr | XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancer |
| title_full_unstemmed | XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancer |
| title_short | XML-GBM lung: An explainable machine learning-based application for the diagnosis of lung cancer |
| title_sort | xml gbm lung an explainable machine learning based application for the diagnosis of lung cancer |
| topic | Lung cancer Explainable machine learning ROS SHAP GBM Mobile app |
| url | http://www.sciencedirect.com/science/article/pii/S2153353923001219 |
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