Main Article Content
Breast cancer, Metastasis, Machine learning, Specificity selection, Classification algorithms, Hybrid algorithm
Background: Breast cancer (BC) is a prevalent disease and a major cause of mortality among women worldwide. A substantial number of BC patients experience metastasis which in turn leads to treatment failure and death. The survival rate has been significantly increased due to more rapid detection and substantial improvements in adjuvant therapies including newer chemotherapeutic and targeted agents, and better radiotherapy techniques.
Methods: In this study, we cross-compared the application of advanced artificial intelligence algorithms such as Logistic Regression, K-Nearest Neighbors, Discrete Cosine Transform, Random Forest Classifier, Support Vector Machines, Multilayer Perceptron, and Ensemble to diagnose BC metastasis. We further combined MLP with genetic algorithm (GA) as a hybrid method of intelligent analysis. The core data we used for comparison belonged to the images of both benign and malignant tumors collected from Wisconsin Breast Cancer dataset from the UCI repository.
Results: The application of several different algorithms to the collection of BC data indicated that these algorithms have comparable accuracy rate in detecting and predicting cancer. However, our hybrid algorithm showed superior accuracy, sensitivity and specificity compared to the individual algorithms. Two methods of comparison (Cross-Validation and Holdout) were applied to this study which produced consistent results.
Conclusion: Our findings indicate that our MLP-GA hybrid algorithm can speed up diagnosis with higher accuracy rate than the individual patterns of algorithm.
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