APRENDIZAJE AUTOMÁTICO EN LA DETECCIÓN DE ENFERMEDADES CORONARIAS: UNA REVISIÓN TÉCNICO-CIENTÍFICA
DOI:
https://doi.org/10.56238/arev7n10-039Palabras clave:
Aprendizaje Automático, Enfermedad Coronaria, AterosclerosisResumen
Las enfermedades cardiovasculares siguen siendo la principal causa de muerte en todo el mundo, lo que hace que los avances en la prevención sean esenciales. Esta revisión resume el trabajo reciente sobre aprendizaje automático (ML) aplicado a datos clínicos estructurados para detectar o predecir la enfermedad de la arteria coronaria (EAC). Esta revisión narrativa se realizó bajo el marco PRISMA 2020. Las búsquedas en PubMed, IEEE Xplore y SciELO (enero de 2020 a abril de 2025) arrojaron 3780 registros. Después de la selección y la evaluación del texto completo, se incluyeron 10 artículos: siete estudios primarios y tres revisiones. Los tamaños de muestra variaron de 303 a 70 000 individuos. Los algoritmos y conjuntos basados en árboles registraron las mejores puntuaciones, con una precisión de entre 0,82 y 0,99 y un AUROC de 0,86 a 0,96. La explicabilidad con SHAP se aplicó en cuatro estudios, y uno emparejó SHAP con LIME. Un artículo añadió la aportación de un cardiólogo al ciclo de decisión, lo que aumentó la precisión de 0,7829 a 0,8302. Solo un artículo evaluó sus modelos con conjuntos de datos externos y observó descensos en el rendimiento. La calibración se abordó en raras ocasiones. Tan solo una investigación informó una puntuación Brier de 0,14 y una pendiente de 0,93. Los modelos de aprendizaje automático (ML) entrenados únicamente con variables demográficas, de laboratorio y clínicas recopiladas rutinariamente muestran una sólida capacidad de clasificación para la enfermedad coronaria (CAD), lo que respalda su uso como herramienta de cribado no invasiva y de apoyo a la toma de decisiones. Se requieren ensayos prospectivos, validación externa e informes de calibración detallados antes de su adopción clínica.
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