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Breast Cancer Classification Using Machine Learning and Neural Network Models: A Comprehensive Comparative Study
SAMBIT SUBHANKAR DAS
1
,
ATAL MAHAPRASAD
1
,
*
2
,
SRIKANT MISRA
1
Academic Editor: Alessandro Lo Schiavo
Published:
03 December 2025
by MDPI
in The 6th International Electronic Conference on Applied Sciences
session Electrical, Electronics and Communications Engineering
Abstract:
- Background: Breast cancer is one of the most fatal cancers among women around the globe. The chances of surviving this cancer increase with early tumor detection, which is necessary for effective treatment. Traditional diagnostic techniques are ineffective and take a long time to identify tumors, and the results may be accurate or inaccurate. Objective: The main objective of this project is to build an ML-based classification model that can help doctors to detect breast cancer early and more accurately. This project also aims to provide an interactive interface for easy accessibility for healthcare usage. Materials/Methods: For this study, twelve Machine Learning Classification Algorithms are implemented and tested: Logistic Regression, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, XGBOOST, Naive Bayes, ADA Boosting, Light GBM, Cat Boost and Artificial Neural Network (ANN). The study used the Wisconsin Breast Cancer Dataset (WBCD) from the UCI ML Repository. It contains 569 patient samples and 30 features. This dataset possesses the following features: Radius, Texture, Area, Perimeter, Smoothness, Compactness, Concavity, and Fractional Dimension. The target variable is Diagnosis, which is categorized as Malignant vs Benign. Results: The fifteen models were analyzed, evaluated and compared using five performance metrics: Accuracy, Precision, Recall, F1-Score and AUC-ROC. Among all the evaluated models, the Artificial Neural Network (ANN) outperformed other models with an accuracy of 97.37.%, with 97% Precision, Recall and F1-Score. The AUC-ROC is nearly 99.61%, meaning that the model is able to differentiate between malignant and benign tumours.
Keywords: Breast Cancer Diagnosis ; Machine Learning Classification; Wisconsin Breast Cancer Dataset (WBCD) ;Malignant vs. Benign; Prediction Comparative Study ; Artificial Neural Network (ANN)
