ArticleJournal of imaging2021
Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis.
Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 68 citations in OpenAlex.
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- Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework.Scientific reports · 2025Article
- A novel aggregated coefficient ranking based feature selection strategy for enhancing the diagnosis of breast cancer classification using machine learning.Scientific reports · 2025Article
- Weighted-VAE: A deep learning approach for multimodal data generation applied to experimental T. cruzi infection.PloS one · 2025Article
- Diagnostic Challenges and Patient Safety: The Critical Role of Accuracy - A Systematic Review.Journal of multidisciplinary healthcare · 2025Review
- Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection.Journal of Cancer · 2025Review
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- A structured combination of ensemble classifier and filter-based feature selection to improve breast cancer diagnosis.Journal of cancer research and clinical oncology · 2023Article
- An integrative machine learning framework for classifying SEER breast cancer.Scientific reports · 2023Article
- Studying infant mortality: A demographic analysis based on data mining models.Open life sciences · 2023Article
- Review
- Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia.Frontiers in oncology · 2022Article
- Predicting hospital readmission risk in patients with COVID-19: A machine learning approach.Informatics in medicine unlocked · 2022Article
- Advanced Computational Methods for Oncological Image Analysis.Journal of imaging · 2021Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is one of the leading causes of death among women, more so than all other cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the disease, changing treatment procedures and different patient population samples. Diagnostic techniques with better performance are very important for personalized care and treatment and to reduce and control the recurrence of cancer. The main objective of this research was to select feature selection techniques using correlation analysis and variance of input features before passing these significant features to a classification method. We used an ensemble method to improve the classification of breast cancer. The proposed approach was evaluated using the public WBCD dataset (Wisconsin Breast Cancer Dataset). Correlation analysis and principal component analysis were used for dimensionality reduction. Performance was evaluated for well-known machine learning classifiers, and the best seven classifiers were chosen for the next step. Hyper-parameter tuning was performed to improve the performances of the classifiers. The best performing classification algorithms were combined with two different voting techniques. Hard voting predicts the class that gets the majority vote, whereas soft voting predicts the class based on highest probability. The proposed approach performed better than state-of-the-art work, achieving an accuracy of 98.24%, high precision (99.29%) and a recall value of 95.89%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.