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Predicting Prostate Cancer Diagnosis Using Machine Learning Analysis of Healthcare Utilization Patterns

  • 0University of Utah, Salt Lake City, Utah, USA.
Studies in Health Technology and Informatics +

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April 9, 2025

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April 9, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Machine learning models can predict prostate cancer up to 6 months in advance using healthcare data. Prostate-Specific Antigen (PSA) levels were the strongest indicator for early cancer detection.

Area Of Science

  • Computational oncology
  • Health informatics
  • Machine learning in healthcare

Background

  • Early prostate cancer detection is crucial for effective treatment and improved patient outcomes.
  • Understanding pre-diagnostic healthcare utilization patterns can inform predictive modeling.
  • The All of Us Research Program provides a rich dataset for studying disease prediction.

Purpose Of The Study

  • To investigate healthcare utilization patterns preceding prostate cancer diagnosis.
  • To develop and evaluate machine learning models for early prostate cancer prediction.
  • To identify key clinical variables predictive of prostate cancer diagnosis.

Main Methods

  • Utilized data from the All of Us Research Program (1,276 cancer patients, 1,232 controls).
  • Extracted features from procedure, measurement, and condition records, including Prostate-Specific Antigen (PSA) levels, comorbidity index, and symptoms.
  • Trained and tested multiple machine learning models (e.g., XGBoost) to predict prostate cancer diagnosis at 3, 6, 9, and 12 months prior.

Main Results

  • The XGBoost model achieved the highest performance at 3 months (Accuracy=0.73, F1=0.73, AUC=0.82) and 6 months (Accuracy=0.71, F1=0.71, AUC=0.78).
  • Predictive performance decreased with longer prediction time windows.
  • Prostate-Specific Antigen (PSA) levels were the most significant predictor, followed by triglyceride and creatinine levels.

Conclusions

  • Machine learning models can effectively predict prostate cancer diagnosis using pre-diagnostic healthcare utilization data.
  • Early prediction is feasible, particularly within a 3-6 month window prior to diagnosis.
  • PSA levels are a critical factor in early prostate cancer prediction models.

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