Precancer Detection Based on Mutations in Codons 248 and 249 Using Decision Tree (DT) and XGBoost Deep Learning Model.
Abuelmakarem, H., Majdy, A., Maher, G., Khaled, H., Emad, M., Asem Shaker, E. (2025). Precancer Detection Based on Mutations in Codons 248 and 249 Using Decision Tree (DT) and XGBoost Deep Learning Model.. EKB Journal Management System, 6(1), 67-77. doi: 10.21608/ijisd.2025.399181
Hala S Abuelmakarem; Ahmed Majdy; George Maher; Hossam Khaled; Malak Emad; Esraa Asem Shaker. "Precancer Detection Based on Mutations in Codons 248 and 249 Using Decision Tree (DT) and XGBoost Deep Learning Model.". EKB Journal Management System, 6, 1, 2025, 67-77. doi: 10.21608/ijisd.2025.399181
Abuelmakarem, H., Majdy, A., Maher, G., Khaled, H., Emad, M., Asem Shaker, E. (2025). 'Precancer Detection Based on Mutations in Codons 248 and 249 Using Decision Tree (DT) and XGBoost Deep Learning Model.', EKB Journal Management System, 6(1), pp. 67-77. doi: 10.21608/ijisd.2025.399181
Abuelmakarem, H., Majdy, A., Maher, G., Khaled, H., Emad, M., Asem Shaker, E. Precancer Detection Based on Mutations in Codons 248 and 249 Using Decision Tree (DT) and XGBoost Deep Learning Model.. EKB Journal Management System, 2025; 6(1): 67-77. doi: 10.21608/ijisd.2025.399181

Precancer Detection Based on Mutations in Codons 248 and 249 Using Decision Tree (DT) and XGBoost Deep Learning Model.

International Journal of Industry and Sustainable Development
Volume 6, Issue 1, January 2025, Page 67-77  XML PDF (694.15 K)
Document Type: Original Article
DOI: 10.21608/ijisd.2025.399181
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Authors
Hala S Abuelmakaremorcid ; Ahmed Majdy; George Maher; Hossam Khaled; Malak Emad; Esraa Asem Shaker
Systems and Biomedical Engineering Department, The Higher Institute of Engineering, El-Shorouk Academy, El-Shorouk City, Cairo, Egypt,11837
Abstract
The mutation serves as a biomarker for cancer diagnosis and prognosis, indicating early-stage cancer. Efforts are underway to develop advanced pre-cancer detection methods and therapeutic strategies to restore TP53 function or counteract its loss. This study evaluates the performance of different deep-learning techniques in mutation detection as a precancer classifier. The evaluation was conducted in two distinct phases. Following data processing and feature selection, the performance of several models—Artificial Neural Network (ANN), Decision Tree (DT), K-Nearest Neighbors (KNN), and Convolutional Neural Network (CNN)—was systematically assessed across accuracy, sensitivity, and specificity metrics for codons 248, 249, and a combined dataset of these codons. Each model’s effectiveness was evaluated under two feature conditions, encompassing eight and thirty-three features. The Decision Tree, identified as the optimal performer, was further enhanced by integrating it with the XGBoost deep learning algorithm to maximize performance. Integrating a DT with XGBoost improves accuracy from 93.15% to 96.55%, sensitivity from 94% to 98%, and specificity from 92% to 96%, making it more effective in detecting precancers based on codon mutations. This combined model enhances both true positive and true negative identification. The detection of codon mutations shows promise for early cancer detection.
Keywords
Precancer Detection; Codon Mutations; Decision Tree; Deep Learning
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