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. 2023 Sep;149(11):9337-9348.
doi: 10.1007/s00432-023-04861-5. Epub 2023 May 19.

An ensemble classifier method based on teaching-learning-based optimization for breast cancer diagnosis

Adila Tuerhong  1 , Mutalipu Silamujiang  2 , Yilixiati Xianmuxiding  3 , Li Wu  4 , Musa Mojarad  5
Affiliations

An ensemble classifier method based on teaching-learning-based optimization for breast cancer diagnosis

Adila Tuerhong et al. J Cancer Res Clin Oncol. 2023 Sep.

Abstract

Introduction: Epidemiological studies show that breast cancer is the most common cancer in women in the world. Breast cancer treatment can be very effective, especially when the disease is detected in the early stages. The goal can be achieved by using large-scale breast cancer data with the machine learning models METHODS: This paper proposes a new intelligent approach using an optimized ensemble classifier for breast cancer diagnosis. The classification is done by proposing a new intelligent Group Method of Data Handling (GMDH) neural network-based ensemble classifier. This method improves the performance of the machine learning technique by using a Teaching-Learning-Based Optimization (TLBO) algorithm to optimize the hyperparameters of the classifier. Meanwhile, we use TLBO as an evolutionary method to address the problem of appropriate feature selection in breast cancer data.

Results: The simulation results show that the proposed method has a better accuracy between 7 and 26% compared to the best results of the existing equivalent algorithms.

Conclusion: According to the obtained results, we suggest the proposed algorithm as an intelligent medical assistant system for breast cancer diagnosis.

Keywords: Breast cancer detection; Ensemble classifier; Evolutionary methods; Feature selection; GMDH; TLBO.

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Conflict of interest statement

We certify that there is no actual or potential conflict of interest in relation to this manuscript.

Figures

Fig. 1
Fig. 1
Flowchart of the proposed algorithm
Fig. 2
Fig. 2
WBCD dataset in two training and testing sets
Fig. 3
Fig. 3
The considered matrix based on the feature pair f1,f2
Fig. 4
Fig. 4
A configuration of GMDH with three layers
Fig. 5
Fig. 5
Confusion matrix for a WBCD dataset, b WPBC dataset, c WDBC dataset
Fig. 6
Fig. 6
Accuracy for a WBCD dataset, b WPBC dataset, c WDBC dataset
Fig. 7
Fig. 7
ROC curve for a WBCD dataset, b WPBC dataset, c WDBC dataset
See this image and copyright information in PMC

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