Artificial neural network applied to prostate cancer diagnosis
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1371Keywords:
Artificial Neural Network, Diagnosis, Prostate cancerAbstract
Objective: Develop a method to assist in the diagnosis of prostate cancer using Artificial Neural Network applied to clinical variables. Method: Retrospective observational research was carried out on 274 medical records from the University Hospital of the Federal University of Maranhão. The following clinical variables were used: age, race, systemic arterial hypertension, diabetes mellitus, smoking, alcohol consumption, digital rectal exam, and total PSA. An Artificial Neural Network model was created for predictive classification. Results: The model presented an accuracy of 80%, sensitivity of 80%, specificity of 80% and area under the ROC curve of 0.9027. Conclusion: Excellent performance was obtained in predicting prostate cancer. This method can be incorporated into clinical practice as doctors and patients can reap the benefits of it by reducing unnecessary biopsies without compromising the ability to diagnose prostate cancer.
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