Búsqueda de el mejor modelo de aprendizaje de máquina para detección de cáncer de mama
Palabras clave:
Cáncer de Mama, Clasificación, Teoría de las Decisiones, Aprendizaje de Máquina, Aprendizaje SupervisadoResumen
El Aprendizaje de Máquina comprende una amplia gama de modelos que pretenden resolver problemas mediante algoritmos Supervisados y No Supervisados, éstos son capaces de encontrar relaciones causales y correlaciones que pueden pasar desapercibidas por otros métodos. Dados los avances tecnológicos, en concreto software, se pueden utilizar estas herramientas a varias disciplinas, como lo es Oncología. Ésta es una especialidad médica que se enfoca en el Cáncer y puede ser beneficiada al utilizar estos modelos para detección de Cáncer de Mama. En el presente artículo, exploramos un catálogo de modelos de Aprendizaje de Máquina Supervisados y estudiamos su eficiencia mediante diferentes criterios, para encontrar el más adecuado para resolver este problema. El método Analytic Hierarchy Process brindó resultados claros, mediante el cuál se asignó al Random Forest como el mejor modelo en los tres análisis que se llevaron a cabo; con una calificación más de 10% más alta que el segundo mejor modelo, la Regresión Logística. Estos modelos fueron entrenados con datos sobre diferentes células de tumores en mamas, por lo que con diferentes datos, los resultados pueden variar.
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