BREAST CANCER PREDICTION WITH GRADIENT BOOSTING CLASSIFIERS

Abstract

Breast cancer poses a significant global health challenge, being the most prevalent cancer in women and a leading cause of cancer-related mortality. With the increasing number of diagnoses, there is a pressing need for innovative approaches to enhance detection and treatment. This study explores the effectiveness of boosting algorithms, namely AdaBoost, XGBoost, CatBoost, and LightGBM, in predicting breast cancer by leveraging artificial intelligence (AI) and machine learning (ML) technologies. This research distinguishes itself from previous studies by focusing specifically on gradient boosting algorithms. The research utilizes datasets sourced from Kaggle, comprising 569 instances categorized into Benign (357) and Malignant (212) classes. Data preprocessing steps, such as feature selection and normalization, were performed to improve the model’s learning ability and ensure that all features contribute fairly during training. Additionally, Hyperparameter tuning, including adjusting the learning rate, number of trees, and tree depth, was conducted to optimize the model’s performance. Following training, various performance metrics including accuracy, sensitivity, precision, specificity, ROC-AUC, and confusion matrix were assessed. The results showed that AdaBoost achieved an accuracy of 97.37%, precision of 0.9545, recall of 0.9767, specificity of 0.9718, and an AUC-ROC of 0.9967. CatBoost achieved an accuracy of 97.49%, precision of 0.9756, recall of 0.9302, specificity of 0.9859, and an AUC-ROC of 0.9977. LightGBM achieved an accuracy of 96.49%, precision of 0.9535, recall of 0.9535, specificity of 0.9718, and an AUC-ROC of 0.9961....