What question did this study set out to answer?

The aim is to compare different machine learning algorithms for classifying breast tumors as malignant or benign.

June 2, 20260 citationsOpen Access

Breast Cancer Classification using Machine Learning and Explainable AI on the Wisconsin Diagnostic Dataset

Key Points

The aim is to compare different machine learning algorithms for classifying breast tumors as malignant or benign.
Evaluated five supervised machine learning algorithms on the Wisconsin Diagnostic Breast Cancer dataset.
Applied feature standardization and stratified train-test splitting.
Employed explainable AI techniques for transparency and understanding of model predictions.
Logistic Regression, MLP, and SVM achieved 97.37% accuracy during classification.
Strong predictive performance indicated by high accuracy rates.
Explainable AI methods increased clinical trust in diagnostic results.

Abstract

Breast cancer remains a leading cause of cancer-related mortality among women globally, underscoring the critical need for accurate and early diagnostic tools. This study presents a comparative analysis of five supervised machine learning algorithms for binary classification of breast tumors as malignant or benign using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Logistic Regression, K-Nearest Neighbors, Decision Tree, Multi-Layer Perceptron, and Support Vector Machine models were evaluated after feature standardization and stratified train-test splitting. Logistic Regression, MLP, and SVM achieved the highest accuracy of 97.37%, demonstrating strong predictive performance. Explainable AI techniques including feature correlation analysis, logistic regression coefficient interpretation, and decision tree rule extraction were employed to enhance transparency and clinical trust. The findings demonstrate the effectiveness of machine learning and explainable AI for supporting breast cancer diagnosis.

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