# On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset

arXiv: https://arxiv.org/abs/1711.07831 · HTML: https://mlsanity.com/paper/1711.07831/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2017.

## Built on by (most cited first, 11 of 11)

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- [Breast Cancer Classification using Deep Learned Features Boosted with Handcrafted Features](https://mlsanity.com/paper/2206.12815/) (2022)
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- [Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI](https://mlsanity.com/paper/2404.04686/) (2024)
- [O2PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection](https://mlsanity.com/paper/2101.05775/) (2021)
- [UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis](https://mlsanity.com/paper/2401.03173/) (2024)
- [Privacy-Preserving Feature Valuation in Vertical Federated Learning Using Shapley-CMI and PSI Permutation](https://mlsanity.com/paper/2512.14767/) (2025)

## Abstract (arXiv)

This paper presents a comparison of six machine learning (ML) algorithms: GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, & Mangasarian, 1992) by measuring their classification test accuracy and their sensitivity and specificity values. The said dataset consists of features which were computed from digitized images of FNA tests on a breast mass (Wolberg, Street, & Mangasarian, 1992). For the implementation of the ML algorithms, the dataset was partitioned in the following fashion: 70% for training phase, and 30% for the testing phase. The hyper-parameters used for all the classifiers were manually assigned. Results show that all the presented ML algorithms performed well (all exceeded 90% test accuracy) on the classification task. The MLP algorithm stands out among the implemented algorithms with a test accuracy of ~99.04%.