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Artificial Intelligence Approaches for Predicting Prostate Cancer

EasyChair Preprint no. 13644

16 pagesDate: June 12, 2024


Prostate cancer is a significant health concern globally, and its early detection and prediction are crucial for successful treatment outcomes. Artificial intelligence (AI) approaches have emerged as promising tools for improving prostate cancer prediction. This abstract provides an overview of AI approaches and their potential in predicting prostate cancer.


Traditional methods for predicting prostate cancer, such as PSA testing and biopsy, have limitations in terms of accuracy and invasiveness. AI offers a range of techniques, including machine learning algorithms and deep learning models, that can leverage various data sources such as medical imaging, electronic health records, genomic data, and biomarkers.


Machine learning algorithms, such as Support Vector Machines, Random Forests, and Logistic Regression, can analyze large datasets and identify patterns indicative of prostate cancer. Unsupervised learning algorithms like clustering and Principal Component Analysis can uncover hidden structures in the data that may be associated with cancer development.

Keyphrases: early detection, predictive models, prostate cancer, risk assessment, Screening

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ayuns Luz},
  title = {Artificial Intelligence Approaches for Predicting Prostate Cancer},
  howpublished = {EasyChair Preprint no. 13644},

  year = {EasyChair, 2024}}
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