Modelo predictivo para la detección precoz de infartos en la población del norte de Perú
DOI:
https://doi.org/10.64439/cisai.v1i1.9Palabras clave:
Redes neuronales artificiales, Modelo predictivo, Infarto de miocardio, Inteligencia artificial, Salud públicaResumen
Las enfermedades cardiovasculares representan una de las principales causas de mortalidad a nivel global, y los infartos de miocardio constituyen una de las manifestaciones más críticas de esta problemática. En la región norte del Perú, la limitada infraestructura hospitalaria y el acceso insuficiente a diagnósticos especializados incrementan la vulnerabilidad de la población. El presente estudio propone el desarrollo de un modelo predictivo basado en Redes Neuronales Artificiales (RNA) para la detección temprana de infartos. Se trabajó con datos secundarios de la encuesta demográfica y de salud familiar del periodo 2005–2024, con un total de 573,080 registros que incluyen factores de riesgo como hipertensión, diabetes, obesidad, tabaquismo y consumo de alcohol. La metodología contempló la limpieza, integración y normalización de datos, seguida de la implementación de un modelo de RNA en Python, validado mediante métricas de precisión, sensibilidad y especificidad. Los resultados preliminares evidencian una precisión predictiva superior al 90%, lo que respalda la viabilidad del uso de inteligencia artificial en la medicina preventiva, particularmente en escenarios caracterizados por una alta demanda asistencial.
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Derechos de autor 2025 Copyright (c) 2025 Henry Silva-Marchan, José Pardo Garces, Raúl Sánchez Ancajima, Gabriela Barrera Rea
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
