Diagnosing Breast Cancer Using AI: A Comparison of Deep Learning and Traditional Machine Learning Methods

Abstract

Breast cancer remains a significant health concern globally, with early detection being crucial for effective treatment. In this study, we explore the predictive power of various diagnostic features in breast cancer using machine learning techniques. We analyzed a dataset comprising clinical measurements of mammograms from 569 patients, including mean radius, texture, perimeter, area, and smoothness, alongside the diagnosis outcome. Our methodology involves preprocessing steps such as handling missing values and removing duplicates, followed by a correlation analysis to identify and eliminate highly correlated features. Subsequently, we train eight machine learning models, including Logistic Regression (LR), K-Nearest Neighbors (K-NN), Linear Support Vector Machine (SVM), Kernel SVM, Naïve Bayes, Decision Trees Classifier (DTC), Random Forest Classifier (RFC), and Artificial Neural Networks (ANN), to predict the diagnosis based on the selected features. Through comprehensive evaluation metrics such as accuracy and confusion matrices, we assess the performance of each model. Our findings reveal promising results, with 6 out of 8 models achieving high accuracy (>90%), with ANN having the highest accuracy in diagnosing breast cancer based on the selected features. These results underscore the potential of machine learning algorithms in aiding early breast cancer diagnosis and highlight the importance of feature selection in improving predictive performance.