Enhanced non-invasive machine learning approach for early colorectal cancer detection: Predictive modeling and validation in a Jordanian cohort.

Journal: Computers in biology and medicine

PMID: 40249989

Abstract

BACKGROUND: Colorectal cancer (CRC) ranks as the third most prevalent cancer worldwide, posing significant public health challenges. Late-stage detection often results in poor treatment outcomes, elevating mortality rates. The economic and psychological burdens of CRC treatment underscore the need for early detection.

Authors

Soha Kanaan

Princess Sumaya University for Technology,(PSUT), Amman, Jordan. Electronic address: sohakanaan@yahoo.com.
Ahmad Altamimi

Department of Software Engineering, Princess Sumaya University for Technology (PSUT), Amman, Jordan.
Hazem Qattous

Department of Software Engineering, Princess Sumaya University for Technology (PSUT), Amman, Jordan.
Haitham Rbeihat

Royal Medical Services, Amman, Jordan.

Keywords

Adult Aged Algorithms Bayes Theorem Colorectal Neoplasms Early Detection of Cancer Female Humans Jordan Machine Learning Male Middle Aged Retrospective Studies Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (40249989)

Enhanced non-invasive machine learning approach for early colorectal cancer detection: Predictive modeling and validation in a Jordanian cohort.

Abstract

Authors

Keywords

External Resources

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