The code analyzes the Haberman dataset, which contains information on patients who underwent surgery for breast cancer. The dataset includes three features: age of the patient at the time of surgery, year of the operation, and the number of positive axillary nodes detected, along with a survival status indicating whether the patient survived 5 years or more after surgery. The primary goal is to predict the survival status of patients using a neural network model. After cleaning and normalizing the data, the model is trained and evaluated on its ability to accurately classify survival outcomes. The model's performance metrics, including accuracy and Mean Squared Error (MSE), provide insight into its predictive power. Despite the challenges inherent in the dataset, the neural network is used to approximate the survival chances of patients based on the available features.

Red de Atención para Cáncer de Mama, en función del Equipamiento para tamizaje, diagnóstico y tratamiento. Región Sur Sureste. (03•sep•2024) Fuente: SINERHIAS 2023, CNEGSR y CNPM 2024. Servicios de Aceleradores y Radioterapia: SINERHIAS, 2023. Elaboración: Coordinación de Normatividad y Planeación Médica / División de Infraestructura Médica, integrando el concepto de Red Nacional de Cáncer (RNC). - Material de trabajo, de uso interno -