Accurate Detection of Breast Cancer Metastasis Using a Hybrid Model of Artificial Intelligence Algorithm

Abstract

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.

The potential application of the protein hydrolysates of three medicinal plants: cytotoxicity and functional properties

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A hybrid method for heart disease diagnosis utilizing feature selection based ensemble classifier model generation

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“…In addition, the F1 score was alive of the accuracy of a binary model. To boot, the efficiency was estimated with F-measure (F1) to check the similarity and variety of efficiency [ 45 , 46 ].…”

“…Specificity: If the result is negative for the person, the result will be negative in a few percent of cases. Which is calculated from the following formula [ 45 , 46 ]. …”

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

“…Performance measurement is used to determine the effectiveness of the classification algorithm so that, in the case of two-dimensional classification problems, one can show the cost of classification with a cost matrix for two types of false positive (FP) and false-negative (FN) errors and two types of classification into the positive true (TN) and negative true (TN) that give different costs and benefits. As shown in Tables 6 and 7 [ 6 , 49 , 50 , 78 ].…”