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

TL;DR

This study compares six machine learning algorithms on the Wisconsin Diagnostic Breast Cancer dataset, finding that all perform well with over 90% accuracy, and MLP achieves the highest at approximately 99%.

Contribution

It provides a comparative analysis of multiple ML algorithms on breast cancer detection, highlighting the superior performance of MLP.

Findings

All algorithms achieved over 90% test accuracy.

MLP outperformed others with ~99.04% accuracy.

The dataset features were effective for classification.

Abstract

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%.