An extreme gradient boosting for cancer feature extraction and classification
Cancer remains a leading cause of death worldwide; the World Health Organization (WHO) reports that there have been nearly 10 million cancer-related deaths in recent years, with breast cancer affecting over 2.1 million women annually on a global scale, posing significant challenges for early detecti...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Insight Society
2025
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| Online Access: | https://umpir.ump.edu.my/id/eprint/45975/ |
| _version_ | 1848827539153223680 |
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| author | Chuan, Teo Voon Moorthy, Kohbalan Nasarudin, Ismail Mohd. Murtadha, Mohamad Howe, Chan Weng |
| author_facet | Chuan, Teo Voon Moorthy, Kohbalan Nasarudin, Ismail Mohd. Murtadha, Mohamad Howe, Chan Weng |
| author_sort | Chuan, Teo Voon |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Cancer remains a leading cause of death worldwide; the World Health Organization (WHO) reports that there have been nearly 10 million cancer-related deaths in recent years, with breast cancer affecting over 2.1 million women annually on a global scale, posing significant challenges for early detection and diagnosis. Gene selection, using DNA microarray data, is crucial for reducing the presence of less informative genes and ensuring the selection of genes relevant to disease diagnosis. Cancer classification involves identifying the type of cancer and determining the extent of tumor growth and spread. This research focuses on improving gene selection for cancer classification using the XGBoost classifier, an efficient open-source implementation of the gradient-boosted trees algorithm. The primary goal is to enhance the performance of gene selection, enabling timely and appropriate treatments for cancer patients, as early detection is vital for ensuring a full recovery. Additionally, this research aims to reduce the time and expense associated with gene selection for cancer classification while increasing classification accuracy. The proposed method achieved an accuracy of approximately 93%, with precision, recall, and F1-score values of 93%, 87%, and 90%, respectively. The study highlights the potential of the XGBoost classifier in optimizing gene selection and improving diagnostic processes. Future work will focus on enhancing the accuracy of gene selection for cancer classification and reducing the number of irrelevant genes before proceeding to subsequent processes. This approach holds promises for streamlining the diagnostic process, improving patient outcomes, and offering significant benefits in timely cancer treatment. |
| first_indexed | 2025-11-15T04:02:19Z |
| format | Article |
| id | ump-45975 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T04:02:19Z |
| publishDate | 2025 |
| publisher | Insight Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-459752025-10-23T01:06:26Z https://umpir.ump.edu.my/id/eprint/45975/ An extreme gradient boosting for cancer feature extraction and classification Chuan, Teo Voon Moorthy, Kohbalan Nasarudin, Ismail Mohd. Murtadha, Mohamad Howe, Chan Weng QA75 Electronic computers. Computer science QH301 Biology RC Internal medicine Cancer remains a leading cause of death worldwide; the World Health Organization (WHO) reports that there have been nearly 10 million cancer-related deaths in recent years, with breast cancer affecting over 2.1 million women annually on a global scale, posing significant challenges for early detection and diagnosis. Gene selection, using DNA microarray data, is crucial for reducing the presence of less informative genes and ensuring the selection of genes relevant to disease diagnosis. Cancer classification involves identifying the type of cancer and determining the extent of tumor growth and spread. This research focuses on improving gene selection for cancer classification using the XGBoost classifier, an efficient open-source implementation of the gradient-boosted trees algorithm. The primary goal is to enhance the performance of gene selection, enabling timely and appropriate treatments for cancer patients, as early detection is vital for ensuring a full recovery. Additionally, this research aims to reduce the time and expense associated with gene selection for cancer classification while increasing classification accuracy. The proposed method achieved an accuracy of approximately 93%, with precision, recall, and F1-score values of 93%, 87%, and 90%, respectively. The study highlights the potential of the XGBoost classifier in optimizing gene selection and improving diagnostic processes. Future work will focus on enhancing the accuracy of gene selection for cancer classification and reducing the number of irrelevant genes before proceeding to subsequent processes. This approach holds promises for streamlining the diagnostic process, improving patient outcomes, and offering significant benefits in timely cancer treatment. Insight Society 2025-01 Article PeerReviewed pdf en cc_by_sa_4 https://umpir.ump.edu.my/id/eprint/45975/1/Teo%2BVoon%2B04%2BAAP.pdf Chuan, Teo Voon and Moorthy, Kohbalan and Nasarudin, Ismail and Mohd. Murtadha, Mohamad and Howe, Chan Weng (2025) An extreme gradient boosting for cancer feature extraction and classification. International Journal on Advanced Science, Engineering and Information Technology, 15 (3). pp. 830-838. ISSN 2088-5334. (Published) https://doi.org/10.18517/ijaseit.15.3.12941 doi:10.18517/ijaseit.15.3.12941 doi:10.18517/ijaseit.15.3.12941 |
| spellingShingle | QA75 Electronic computers. Computer science QH301 Biology RC Internal medicine Chuan, Teo Voon Moorthy, Kohbalan Nasarudin, Ismail Mohd. Murtadha, Mohamad Howe, Chan Weng An extreme gradient boosting for cancer feature extraction and classification |
| title | An extreme gradient boosting for cancer feature extraction and classification |
| title_full | An extreme gradient boosting for cancer feature extraction and classification |
| title_fullStr | An extreme gradient boosting for cancer feature extraction and classification |
| title_full_unstemmed | An extreme gradient boosting for cancer feature extraction and classification |
| title_short | An extreme gradient boosting for cancer feature extraction and classification |
| title_sort | extreme gradient boosting for cancer feature extraction and classification |
| topic | QA75 Electronic computers. Computer science QH301 Biology RC Internal medicine |
| url | https://umpir.ump.edu.my/id/eprint/45975/ https://umpir.ump.edu.my/id/eprint/45975/ https://umpir.ump.edu.my/id/eprint/45975/ |