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Cancer Survival Analysis01:21

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Identification of Factors Affecting Prostate Cancer Using Machine Learning Methods: A Systematic Review.

Serveh Mohammadi1, Behzad Imani2, Soheila Saeedi3

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This summary is machine-generated.

Machine learning effectively identifies key prostate cancer risk factors like age and PSA levels. This review highlights top algorithms and data sources for improved prevention and screening strategies.

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Machine LearningPRISMAPSAProstatic NeoplasmsSystematic review

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Area of Science:

  • Oncology
  • Data Science
  • Bioinformatics

Background:

  • Prostate cancer is a leading global malignancy, necessitating effective prevention and screening.
  • Identifying risk factors is crucial for managing the rising incidence and mortality rates.
  • Artificial intelligence and machine learning offer advanced tools for risk factor analysis.

Purpose of the Study:

  • To systematically review machine learning applications in identifying prostate cancer risk factors.
  • To determine the most frequently used machine learning methods and data sources in this field.
  • To guide future research for enhanced prostate cancer prevention and screening.

Main Methods:

  • Systematic review of articles from PubMed, Scopus, Web of Science, and IEEE Xplore (2015-2024).
  • Adherence to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
  • Inclusion criteria focused on studies using machine learning to investigate prostate cancer risk factors.

Main Results:

  • China leads research in machine learning for prostate cancer risk factors.
  • Key identified risk factors include age, prostate-specific antigen (PSA), total PSA (tPSA), free PSA (fPSA), and PSA density (PSAD).
  • Random forest, support vector machine, and logistic regression were prevalent ML methods; R and Python were common analysis tools. SEER, PLCO, and NCBI were frequently used data sources.

Conclusions:

  • Machine learning provides powerful tools for identifying significant prostate cancer risk factors.
  • Utilizing registered data sources and advanced ML methods can improve risk prediction.
  • Findings support enhanced strategies for prostate cancer prevention and early detection.