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Rodriguez-Bigas, M.A., Boland, C.R., Hamilton, S.R., Henson, D.E., Srivastava, S., Jass, J.R., et al. (1997) A National Cancer Institute Workshop on Hereditary Nonpolyposis Colorectal Cancer Syndrome: Meeting Highlights and Bethesda Guidelines. JNCI Journal of the National Cancer Institute, 89, 1758-1762.
https://doi.org/10.1093/jnci/89.23.1758

has been cited by the following article:

• TITLE: Advancing Early Detection of Colorectal Adenomatous Polyps via Genetic Data Analysis: A Hybrid Machine Learning Approach

  AUTHORS: Ahmed S. Maklad, Mohamed A. Mahdy, Amer Malki, Noboru Niki, Abdallah A. Mohamed

  KEYWORDS: Colorectal Adenoma Detection, Colorectal Cancer Diagnosis, Hybrid Machine Learning, Genetics Analysis

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.7, July 24, 2024

  ABSTRACT: In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.