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Machine Learning for Risk Prediction of Oesophago-Gastric Cancer in Primary Care: Comparison with Existing

Emma Briggs1, Marc de Kamps1,2,3, Willie Hamilton4

  • 1School of Computing, University of Leeds, Leeds LS2 9JT, UK.

Cancers
|October 27, 2022
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Summary
This summary is machine-generated.

Machine learning models can significantly improve early diagnosis of oesophago-gastric cancer in primary care. These advanced tools outperform current risk assessment, identifying more potential cancer cases sooner.

Keywords:
cancer diagnosisearly detectionelectronic health recordmachine learningoesophago-gastric cancerprimary carerisk-assessment

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Area of Science:

  • Oncology
  • Medical Informatics
  • Machine Learning

Background:

  • Early diagnosis of oesophago-gastric cancer is challenging due to non-specific symptoms.
  • Current primary care risk-assessment tools have limitations in detecting early-stage disease.

Purpose of the Study:

  • To investigate the potential of machine learning (ML) to enhance diagnostic performance for oesophago-gastric cancer in primary care.
  • To compare ML models against existing risk-assessment tools using electronic health record data.

Main Methods:

  • Utilized a UK primary care electronic health record dataset (7471 cases, 32,877 controls).
  • Developed and evaluated five probabilistic ML classifiers: Support Vector Machine, Random Forest, Logistic Regression, Naïve Bayes, and Extreme Gradient Boosted Decision Trees.
  • Included demographics, symptoms, and lab results as features.

Main Results:

  • Logistic Regression, Support Vector Machine, and Extreme Gradient Boosted Decision Trees achieved high performance (0.89 accuracy, 0.87 AUROC).
  • ML models outperformed the current UK oesophago-gastric cancer risk-assessment tool (ogRAT).
  • ML identified 11.0% to 25.0% more cancer patients than ogRAT, with minimal impact on false positives.

Conclusions:

  • Machine learning holds significant promise for improving primary care cancer risk-assessment tools.
  • ML can aid clinicians in identifying additional oesophago-gastric cancer cases earlier.
  • Earlier detection through ML could potentially lead to improved patient survival outcomes.