ArticleJournal of cancer research and clinical oncology2023
An ensemble classifier method based on teaching-learning-based optimization for breast cancer diagnosis.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 3 citations in OpenAlex.
- Modified teaching learning based optimization with hummingbird flight strategy to solve the single and multi-area dynamic economic dispatch.Scientific reports · 2026Article
- A structured combination of ensemble classifier and filter-based feature selection to improve breast cancer diagnosis.Journal of cancer research and clinical oncology · 2023Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
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Abstract
introductionEpidemiological 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
methodsThis 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.
resultsThe 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.
conclusionAccording to the obtained results, we suggest the proposed algorithm as an intelligent medical assistant system for breast cancer diagnosis.
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