ArticleJournal of cancer research and clinical oncology2023
Automatic breast cancer diagnosis based on hybrid dimensionality reduction technique and ensemble classification.
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 10 papers.
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10 citing papers in PubMed, 26 citations in OpenAlex.
- Deep Generative and Graph-Based Representation Learning for Multiomics Survival Stratification in Ovarian Cancer: Secondary Analysis.JMIR bioinformatics and biotechnology · 2026Article
- Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification.Scientific reports · 2025Article
- Improving hepatocellular carcinoma diagnosis using an ensemble classification approach based on Harris Hawks Optimization.Heliyon · 2024Article
- A semi-supervised ensemble clustering algorithm for discovering relationships between different diseases by extracting cell-to-cell biological communications.Journal of cancer research and clinical oncology · 2024Article
- Multimodal Early Birth Weight Prediction Using Multiple Kernel Learning.Sensors (Basel, Switzerland) · 2023Article
- Toward improving the performance of learning by joining feature selection and ensemble classification techniques: an application for cancer diagnosis.Journal of cancer research and clinical oncology · 2023Article
- A structured combination of ensemble classifier and filter-based feature selection to improve breast cancer diagnosis.Journal of cancer research and clinical oncology · 2023Article
- Data mining techniques in breast cancer diagnosis at the cellular-molecular level.Journal of cancer research and clinical oncology · 2023Article
- An ensemble classifier method based on teaching-learning-based optimization for breast cancer diagnosis.Journal of cancer research and clinical oncology · 2023Article
- Combining ensemble classification and integrated filter-evolutionary search for breast cancer diagnosis.Journal of cancer research and clinical oncology · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionFeature selection in the face of high-dimensional data can reduce overfitting and learning time, and at the same time improve the accuracy and efficiency of the system. Since there are many irrelevant and redundant features in breast cancer diagnosis, removing such features leads to more accurate prediction and reduced decision time when dealing with large-scale data. Meanwhile, ensemble classifiers are powerful techniques to improve the prediction performance of classification models, where several individual classifier models are combined to achieve higher accuracy.
methodsIn this paper, an ensemble classifier algorithm based on multilayer perceptron neural network is proposed for the classification task, in which the parameters (e.g., number of hidden layers, number of neurons in each hidden layer, and weights of links) are adjusted based on an evolutionary approach. Meanwhile, this paper uses a hybrid dimensionality reduction technique based on principal component analysis and information gain to address this problem.
resultsThe effectiveness of the proposed algorithm was evaluated based on the Wisconsin breast cancer database. In particular, the proposed algorithm provides an average of 17% better accuracy compared to the best results obtained from the existing state-of-the-art methods.
conclusionExperimental results show that the proposed algorithm can be used as an intelligent medical assistant system for breast cancer diagnosis.
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