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Updated: May 7, 2026

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Predicting Prostate Cancer Risk and Its Associated Factors Using Machine Learning Techniques: A Retrospective Study.

Serveh Mohammadi1, Behzad Imani2, Soheila Saeedi3

  • 1Department of Operating Room, Mahabad School of Nursing Urmia University of Medical Sciences Urmia Iran.

Health Science Reports
|May 6, 2026
PubMed
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This summary is machine-generated.

Machine learning models effectively predict prostate cancer (PCa) risk factors. Key predictors include PSA levels, hemoglobin, BMI, and fish consumption, aiding personalized risk identification.

Area of Science:

  • Medical Informatics
  • Oncology
  • Machine Learning

Background:

  • Prostate cancer (PCa) is a significant global health concern.
  • Artificial intelligence (AI) and machine learning (ML) are increasingly utilized for disease prediction and diagnosis.
  • Identifying PCa risk factors is crucial for early detection and management.

Purpose of the Study:

  • To apply ML techniques for predicting PCa.
  • To identify the most significant risk factors associated with PCa development.
  • To evaluate the performance of various ML algorithms in PCa prediction.

Main Methods:

  • A retrospective study involving 597 patient records from Shahid Beheshti Hospital, Iran.
  • Literature search across major databases (Web of Science, Scopus, PubMed) from 2000-2024 for risk factors.
Keywords:
artificial intelligencemachine learning algorithmsprostatic neoplasmsrisk factors

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  • Development and evaluation of ML models including Logistic Regression, Gradient Boosting, Random Forest, XGBoost, Support Vector Machine, and Neural Networks.
  • Performance assessment using accuracy, precision, recall, and F1-score.

    • Main Results:

    • The XGBoost model demonstrated the highest predictive performance with 77.5% accuracy, 74.5% sensitivity, and 79.7% specificity.
    • Significant predictors for PCa included total and free prostate-specific antigen (PSA) levels, hemoglobin, Body Mass Index (BMI), and fish consumption.
    • The study identified 49 potential risk factors for PCa.

    Conclusions:

    • ML models can enhance the personalized identification of PCa risks.
    • Further research is needed to refine ML algorithms and mitigate data biases for improved PCa prediction.
    • The findings highlight the potential of ML in understanding and managing PCa.