Prediction Model for Assessing the Risk of Colorectal Cancer Based On Clinical Features and Serum Biomarkers

Shoufia Jabeen Mubarak¹ , Hemamalini Vedagiri²

1. Medical Genomics Lab, Dept. of Bioinformatics, Bharathiar University, Coimbatore, India.
2. Medical Genomics Lab, Dept. of Bioinformatics, Bharathiar University, Coimbatore, India.

Section:Research Paper, Product Type: Journal-Paper
Vol.11 , Issue.2 , pp.1-7, Jun-2024

Online published on Jun 30, 2024

Copyright © Shoufia Jabeen Mubarak, Hemamalini Vedagiri . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Colorectal cancer (CRC) is the most prevalent malignant tumor connected with higher mortality rate in the world. Aberrant metabolic process invigorates the uncontrolled proliferation of cancer cells leading to higher risk for cancer. Serum carcinoembryonic antigen (CEA) levels are considered as positive tumor marker for predicting the risk and pathological stages of CRC. In addition, many experimental studies strongly indicate elevated fatty acid synthase (FASN) levels in CRC patients. However, prediction parameters for diagnosing the patients affected with CRC has many limitations. In order to overcome these restraints, we have modeled an appropriate algorithm to identify patients at high risk of CRC by specifically monitoring the FASN levels. The aim of this paper primarily focuses on envisaging a suitable mathematical model for CRC based on prediction factors such as ranking prediction, top most selection, mathematical values as well novel algorithmic approach for elucidation of FASN and CEA levels by targeting the disease conditions that would enhance the model in order to avoid real time complexity as well as to identify the most leading risky factors of colorectal cancer. This novel algorithm based on consistent method will provide a paradigm to enable the prediction of CRC tumor stages along with high risky features associated with the patients.

Key-Words / Index Term :
Colorectal cancer, CEA, FASN, Top most selection, Feature selection, Algorithm

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