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Study on ensemble model with weight allocation based on improved dung beetle optimization algorithm for screening

Zhou Yu1, Jianping Wang1, Ping Li2

  • 1Department of Colorectal and Anal Surgery, Jinhua Municipal Central Hospital, Jinhua, China.

Journal of Gastrointestinal Oncology
|November 12, 2025
PubMed
Summary

This study introduces a new machine learning model, MSADBO-WV, for early colorectal cancer (CRC) screening using routine lab tests. The model shows high accuracy, improving early detection and patient outcomes.

Keywords:
Ensemble modeldung beetle optimizationlaboratory test indicatorsscreening colorectal cancer (screening CRC)weight allocation

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Area of Science:

  • Oncology
  • Bioinformatics
  • Machine Learning

Background:

  • Early screening for colorectal cancer (CRC) is vital for survival and cost reduction.
  • Current CRC detection methods lack accuracy, sensitivity, and universality.
  • Advanced methods are needed to enhance early CRC screening performance.

Purpose of the Study:

  • To develop an advanced machine learning approach for early CRC screening.
  • To utilize routine laboratory test indicators for improved CRC detection.
  • To enhance the accuracy and effectiveness of early-stage CRC identification.

Main Methods:

  • Explored classification effects of common machine learning methods on CRC using lab indicators.
  • Compared integrated models and proposed a weighted voting strategy (MSADBO-WV).
  • Performed feature selection on 45 dataset features for CRC prediction.

Main Results:

  • The MSADBO-WV method outperformed other integrated and ordinary machine learning methods.
  • Achieved highest accuracy of 98.42%±1.53% with 26 selected features.
  • Demonstrated the analytical framework's utility for effective CRC screening.

Conclusions:

  • MSADBO-WV shows promising performance for colorectal cancer screening.
  • Further evaluation in prospective clinical studies is warranted.
  • The method can aid in early CRC screening and prevention strategies.