Machine Learning-Based System for Colorectal Cancer Prediction

Ibtissam Samnane, Rachid Ed-Daoudi (), Samir Bouali, M’barek El Haloui and Badia Ettaki
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Ibtissam Samnane: Rabat-Instituts, LyRICA: Laboratory of Research in Informatics, Data Sciences and Artificial Intelligence, School of Information Sciences
Rachid Ed-Daoudi: Rabat-Instituts, LyRICA: Laboratory of Research in Informatics, Data Sciences and Artificial Intelligence, School of Information Sciences
Samir Bouali: Rabat-Instituts, LyRICA: Laboratory of Research in Informatics, Data Sciences and Artificial Intelligence, School of Information Sciences
M’barek El Haloui: Rabat-Instituts, LyRICA: Laboratory of Research in Informatics, Data Sciences and Artificial Intelligence, School of Information Sciences
Badia Ettaki: Rabat-Instituts, LyRICA: Laboratory of Research in Informatics, Data Sciences and Artificial Intelligence, School of Information Sciences

A chapter in Technological Innovations for Sustainable Development, 2025, pp 369-381 from Springer

Abstract: Abstract With the increase in the number of diagnosed cases of colon cancer and the high associated mortality rate, it becomes imperative to offer a rapid and effective diagnosis in order to improve patient care and optimize the chances of treatment. In this context, with the challenges in the field of oncology, the need of the application of Artificial Intelligence (AI) through techniques such as Machine Learning (ML) to offer solutions that will help doctors in their work by minimizing time and effort, within the framework of what is called precision medicine. This work will present a system for predicting the type of cancer according to the clinical and demographic factors of patients using ML models. The evaluation of five ML models (Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNNs), XGBoost and Decision Tree Classifier (DTC)) indicated that DTC is the best model. And to facilitate the prediction operation, the model identifier is integrated into a web interface that allows users to fill out forms with the requested factors so that the system gives its prediction on the type of cancer only on these factors without the need to do other medical examinations that take time and delay the treatment time.

Keywords: Colon cancer; Oncology; Artificial Intelligence; Machine Learning; Prediction System (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-032-06725-8_31

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