Red neuronal artificial aplicada al diagnóstico del cáncer de próstata
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1371Palabras clave:
Red Neural Artificial, Diagnóstico, Cáncer de próstataResumen
Objetivo: Desarrollar un método para ayudar en el diagnóstico del cáncer de próstata utilizando Rede Neuronal Artificial aplicadas a variables clínicas. Método: Se realizó una investigación observacional retrospectiva en 274 prontuarios del Hospital Universitario de la Universidad Federal de Maranhão. Se utilizaron las variables clínicas: edad, raza, hipertensión arterial sistémica, diabetes mellitus, tabaquismo, consumo de alcohol, examen de tacto rectal y PSA total. Se creó un modelo de Red Neuronal Artificial para la clasificación predictiva. Resultados: El modelo presentó una precisión del 80%, sensibilidad del 80%, especificidad del 80% y área bajo la curva ROC de 0,9027. Conclusión: Se obtuvo excelente desempeño en la predicción del cáncer de próstata. Este método se puede incorporar a la práctica clínica, ya que los médicos y los pacientes pueden aprovechar sus beneficios al reducir las biopsias innecesarias sin comprometer la capacidad de diagnosticar el cáncer de próstata.
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