Application of Artificial Neural Network Algorithm with Principal Component Analysis for Diagnosis of Breast Cancer Tumors

Cancer is a health disorder where abnormal cells proliferate uncontrollably and is the second leading cause of death worldwide. Breast cancer, in particular, is prevalent among women in Indonesia. This study aims to diagnose breast cancer, identifying whether it is malignant or benign, using Artificial Neural Network (ANN) algorithms to enhance the accuracy of tumor diagnosis. The fundamental principle is to develop a neural network capable of processing information efficiently without relying on Python packages such as scikit-learn. The ANN operates through forward propagation and backward propagation to optimally predict outcomes and update weights. The dataset used is from the UCI Machine Learning Repository, consisting of 569 samples and 30 features. This dataset is divided into a training set (80%) and a cross-validation set (20%). The ANN model comprises one input layer, two hidden layers, and one output layer, utilizing tanh activation functions for the hidden layers and a sigmoid activation function for the output layer. Training results indicated an accuracy of 95.6% on the training set and 93.2% on the cross-validation set. This demonstrates that the model performs well in detecting breast cancer, with a low error rate and strong generalization capability. This study successfully developed an effective and reliable ANN model for breast cancer detection with high accuracy, supporting clinical breast cancer diagnosis.