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On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset
Abien Fred Agarap
TL;DR
Breast cancer is a major research topic, and medical data science and machine learning may assist practitioners’ decision making. This paper compares machine learning algorithms, including GRU-SVM, for breast cancer classification using WDBC, finding high performance across the presented algorithms.
Problem
Breast cancer represents 15% of new cancer cases in the United States, while growing clinical data support research into machine learning for medical decision making.
Method
The paper applies GRU-SVM, which combines a gated recurrent unit with support vector machine prediction, alongside nearest-neighbor distance methods for breast cancer classification on WDBC.
Results
All presented machine learning algorithms exhibited high performance on binary breast cancer classification, while L2-SVM achieved ≈96.09% test accuracy in this study.
Takeaways & Limitations
The results support the effectiveness of machine learning algorithms for diagnosing whether a breast tumor is benign or malignant.
Takeaways & Limitations
The comparison with prior SVM results is not fully fair because the studies used different training/testing partitions: 70/30 here versus 60/40 previously.
Abstract
from arXiv · showhide
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%.
1 INTRODUCTION
Breast cancer is a major research concern, and prior work has applied machine learning to classify tumors using the WDBC dataset. This paper extends that line of study by introducing GRU-SVM alongside other classifiers.
- Breast cancer is among the most common cancers and represents 15% of all new cancer cases in the United States.
- Machine learning may assist medical practitioners’ decision-making and support research using the growing volume of breast-cancer data.
- Prior studies applied machine-learning algorithms to breast-cancer classification with the WDBC dataset and reported significant results.
- This paper introduces GRU-SVM and applies it with other machine-learning algorithms to breast-cancer classification using WDBC.GRU-SVM combines a gated recurrent unit with a support vector machine.
2.1 Machine Intelligence Library
The study implemented its machine-learning algorithms using Google TensorFlow and supporting scientific-computing libraries.
- Google TensorFlow implemented the machine-learning algorithms, supported by matplotlib, numpy, and scikit-learn.
2.2 The Dataset
The WDBC dataset represents breast-mass FNA images through measurements describing cell-nucleus characteristics. It contains 569 data points and 30 derived features.
- WDBC features were computed from digitized fine-needle-aspirate images of breast masses and describe cell-nucleus characteristics.
- Figure 1 shows digitized FNA images labeled benign and malignant.
- The dataset contains 569 data points: 212 malignant and 357 benign.
- The dataset includes radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension.
2.3 Dataset Preprocessing
Preprocessing standardized the dataset to avoid inappropriate relevance assignment, used thresholded linear regression as a classifier, and measured model loss with mean squared error.
- The dataset was standardized before modeling to avoid inappropriate assignment of feature relevance.
- Linear regression was converted into a classifier by thresholding its output.
- Mean squared error was used to measure model loss.
- Standardization uses each feature’s mean and standard deviation and was implemented with scikit-learn’s StandardScaler().fit_transform().
2.4 Machine Learning (ML) Algorithms
The paper applies several ML algorithms to breast cancer classification, including a GRU-SVM architecture that feeds GRU representations into an L2-SVM predictor. The section also describes the classifiers’ optimization procedures, distance measures, and decision outputs.
- The study implements ML algorithms including GRU-SVM, Nearest Neighbor search, Softmax Regression, SVM, MLP, and Linear Regression.
- GRU-SVM: GRU-SVM uses the GRU cell state h_t as the L2-SVM predictor input instead of a conventional Softmax classifier.
- GRU-SVM: The GRU-SVM learning parameter is optimized with L2-SVM loss and Adam, producing scores mapped to benign (-1) or malignant (+1) diagnoses.
- Nearest Neighbor: Nearest Neighbor search identifies the closest point using both L1 Manhattan and L2 Euclidean distances.
- Softmax Regression produces class probabilities, whereas Linear Regression produces raw class scores that require thresholding for classification.
- Support Vector Machine: SVM classification uses a separating hyperplane, with C controlling the penalty and L2-SVM providing differentiable, more stable results than L1-SVM.
2.5 Data Analysis
The experiment separates training and testing into two phases using a 70%/30% dataset partition and evaluates classification with accuracy, error rates, and true-positive and true-negative rates.
- 70% of the dataset was used for training and 30% for testing.
- The experiment had separate training and test phases.
- Evaluation parameters included Test Accuracy, Epochs, data-point count, FPR, FNR, TPR, and TNR.
3 RESULTS AND DISCUSSION
The experiments compare ML classifiers on WDBC using a 70%/30% training/testing split, with results summarized through accuracy and visualized feature separability. All reported algorithms performed strongly, while MLP and L2-SVM achieved particularly high accuracy; comparisons with prior SVM results are constrained by different data partitions.
- Test comparison: 96.09% test accuracy for L2-SVM exceeded the 89.28% reported for Gaussian-RBF SVM in the prior study.The current study used 70% training data, compared with 60% in the prior study.
- Training results: 97.734375% average training accuracy was achieved by L2-SVM, followed by Softmax Regression at 97.366573% and MLP at 96.9286785%.Linear Regression reached 92.8906257%, while GRU-SVM reached 90.6857639%; Nearest Neighbor had no recorded training accuracy because it requires no training.
- Model discussion: GRU-SVM achieved 93.75% test accuracy, with its mid-level performance hypothetically attributed to GRU nonlinearities and sensitivity to arbitrary weight initialization.The paper also states that arbitrary initialization may limit result reproducibility under identical configurations.
- Feature visualization: The WDBC features appear visually separable by a linear function in scatter plots of mean, error, and worst features.Figures 3–5 show feature groups x0−x9, x10−x19, and x20−x29, respectively.
4 CONCLUSION AND RECOMMENDATION
The study concludes that the evaluated machine-learning algorithms performed strongly on binary breast-cancer classification, while recommending cross-validation to strengthen performance estimates and hyper-parameter selection.
- Conclusion: All presented machine-learning algorithms exhibited high performance in distinguishing benign from malignant tumors.The paper also reports satisfactory statistical measures for the classification problem.
- Recommendation: k-fold cross-validation should be employed to provide a more accurate measure of model prediction performance.The authors also state that it could help determine more optimal hyper-parameters for the algorithms.