Mata kuliah: Statistik Spasial Email: Patricia22001@mail.unpad.ac.id

statplot

statplot

various plots in ggplot2

This dataset contains information about patients who underwent breast cancer surgery, including their age, year of operation, number of positive axillary nodes, and survival status. We implemented an Artificial Neural Network to predict survival in breast cancer patients using the Haberman's dataset, which contains 306 cases with three input features (age, operation year, and number of positive nodes) and a binary survival outcome. The data was preprocessed by scaling features and split into 70% training and 30% testing sets. The neural network architecture consisted of two hidden layers (5 and 3 neurons respectively) with sigmoid activation functions. The model achieved moderate performance with approximately 73% accuracy, 70% precision, 65% recall, and an F1 score of 67%. The number of positive lymph nodes emerged as the strongest predictor of survival, while age and operation year had less influence. This historical dataset (1958-1970) provided valuable insights, though the model's performance suggests that additional modern medical factors would be needed for more accurate predictions. The implementation demonstrates the potential of machine learning in medical prognosis while highlighting the importance of comprehensive clinical assessment beyond just the available features.

Performing PCA and plotting biplot and dotplot