Breast Cancer Prognosis Prediction Using Hybrid Artificial Neural Network and Gaussian Naïve Bayes

Abstract

Breast cancer is one of the types of cancer that causes the most deaths in women in the world. Breast cancer prognosis is important to assist medical personnel in predicting the possibility of recurrence so that treatment can be provided more effectively. This study aims to implement a hybrid Artificial Neural Network (ANN) and Gaussian Naïve Bayes method for breast cancer prognosis prediction using the Breast Cancer Wisconsin Prognostic (WPBC) dataset. The dataset consisted of 198 patient records with 35 numerical features. The research stages included data preprocessing, normalization, splitting the dataset into training and testing data using an 80:20 ratio, feature extraction using ANN, and classification using Gaussian Naïve Bayes. Unlike previous studies that generally used single methods, this study utilizes ANN as a feature extractor before the classification process using Gaussian Naïve Bayes. ANN was used with one hidden layer containing 16 neurons to learn non-linear relationships among features before the classification process. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The experimental results showed that the hybrid ANN-Gaussian Naïve Bayes method achieved an accuracy of 90%, precision of 72.73%, recall of 88.89%, and an F1-score of 80%. These results indicate that the hybrid method provides better classification performance compared to single methods in breast cancer prognosis prediction.