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Updated: Aug 10, 2025

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Machine learning-based colorectal cancer prediction using global dietary data.

Hanif Abdul Rahman1,2, Mohammad Ashraf Ottom3,4, Ivo D Dinov3

  • 1University of Michigan, Ann Arbor, USA. hanifr@umich.edu.

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|February 11, 2023
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Summary

Machine learning models, particularly artificial neural networks (ANNs), effectively predict colorectal cancer (CRC) in diverse populations. These ANNs trained on large datasets show promise for improving early detection and interventions in both younger and older adults.

Keywords:
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Area of Science:

  • Oncology
  • Bioinformatics
  • Data Science

Background:

  • Colorectal cancer (CRC) is a leading global cancer diagnosis, with screening increasingly identifying younger adults.
  • Existing machine learning (ML) algorithms, often trained on limited data from older adults, may lack efficacy for broad population screening.
  • The need for robust ML models capable of handling diverse demographics and large datasets is critical for effective CRC detection.

Purpose of the Study:

  • To assess the performance of various ML algorithms on extensive, diverse datasets for colorectal cancer (CRC) prediction.
  • To identify optimal ML models that accurately classify both CRC and non-CRC cases across different age groups and sociodemographic backgrounds.
  • To evaluate ML algorithms using a large, aggregated dataset encompassing dietary information and other relevant data sources.

Main Methods:

  • Utilized a large-scale dataset of 109,343 participants from a global dietary-based CRC study.
  • Augmented the primary dataset with publicly available information from multiple sources to create a comprehensive database.
  • Evaluated nine supervised and unsupervised machine learning algorithms on the aggregated dataset to predict CRC phenotypes.

Main Results:

  • Both supervised and unsupervised ML models demonstrated strong performance in predicting CRC and non-CRC phenotypes.
  • An artificial neural network (ANN) based prediction model emerged as the optimal algorithm.
  • The optimal ANN model achieved a low misclassification rate, with 1% for CRC and 3% for non-CRC cases.

Conclusions:

  • ANN models trained on large, heterogeneous datasets are suitable for both younger and older adults.
  • These models offer a robust foundation for clinical decision support systems in dietary-related, non-invasive CRC screening.
  • Optimized ML algorithms, combined with high screening compliance, can significantly enhance early diagnosis and treatment success rates for CRC.