ArticleIranian journal of basic medical sciences2016
Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets.
Article in Iranian journal of basic medical sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- A Deep Learning and Explainable Artificial Intelligence based Scheme for Breast Cancer Detection.Scientific reports · 2025Article
- A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.Scientific reports · 2025Article
- CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection.Scientific reports · 2024Article
- Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction.PloS one · 2024Article
- Hierarchical Harris hawks optimizer for feature selection.Journal of advanced research · 2023Article
- BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images.Diagnostics (Basel, Switzerland) · 2023Article
- Accurate breast cancer diagnosis using a stable feature ranking algorithm.BMC medical informatics and decision making · 2023Article
- Breast cancer diagnosis using the fast learning network algorithm.Frontiers in oncology · 2023Article
- Effective hybrid feature selection using different bootstrap enhances cancers classification performance.BioData mining · 2022Article
- A Robust Design-Based Expert System for Feature Selection and COVID-19 Pandemic Prediction in Japan.Healthcare (Basel, Switzerland) · 2022Article
- Feature Selection and Molecular Classification of Cancer Phenotypes: A Comparative Study.International journal of molecular sciences · 2022Article
- Determining the effective factors in predicting diet adherence using an intelligent model.Scientific reports · 2022Article
- Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer.PeerJ. Computer science · 2021Article
- Neural Network-Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study.JMIR medical informatics · 2020Article
- A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications.Healthcare (Basel, Switzerland) · 2020Article
- Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers.Sensors (Basel, Switzerland) · 2020Article
- Correlation-Based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network.Computational and mathematical methods in medicine · 2019Article
- Comparison of Bayes Classifiers for Breast Cancer ClassificationAsian Pacific journal of cancer prevention : APJCP · 2018Article
- Development of Spectral Disease Indices for 'Flavescence Dorée' Grapevine Disease Identification.Sensors (Basel, Switzerland) · 2017Article
- Modified Bat Algorithm for Feature Selection with the Wisconsin Diagnosis Breast Cancer (WDBC) DatasetAsian Pacific journal of cancer prevention : APJCP · 2017Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. MATERIALS AND
methodsTo evaluate effectiveness of proposed feature selection method, we employed three different classifiers artificial neural network (ANN) and PS-classifier and genetic algorithm based classifier (GA-classifier) on Wisconsin breast cancer datasets include Wisconsin breast cancer dataset (WBC), Wisconsin diagnosis breast cancer (WDBC), and Wisconsin prognosis breast cancer (WPBC).
resultsFor WBC dataset, it is observed that feature selection improved the accuracy of all classifiers expect of ANN and the best accuracy with feature selection achieved by PS-classifier. For WDBC and WPBC, results show feature selection improved accuracy of all three classifiers and the best accuracy with feature selection achieved by ANN. Also specificity and sensitivity improved after feature selection.
conclusionThe results show that feature selection can improve accuracy, specificity and sensitivity of classifiers. Result of this study is comparable with the other studies on Wisconsin breast cancer datasets.
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27403253PMC4923467What OpenQuestion holds
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