Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Comparison of Different Classifiers for Prediction of Breast Cancer Metastasis in Microarray Analysis



Amini Z1, 2 ; Mehridehnavi A1, 2
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Bioelectric and Biomedical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Department of Bioelectric and Biomedical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Journal of Isfahan Medical School Published:2014

Abstract

Background: In this research, we investigated the performance of some different classifiers for prediction of metastasis in breast cancer. Methods: We used the DNA microarrays of primary breast tumors of 78 young patients. Among these patients, 34 had developed distant metastases within 5 years (poor prognosis group) and 44 formed good prognosis group. For analysis, we applied three different classifiers including support vector machine (SVM), stepwise linear discriminant analysis (SWLDA) and K-nearest neighbors (KNN) classifier. Each of these classifiers used 231 selected genes as an input feature vector and their performances were estimated via using leave one out (LOO) method to classify patients into two groups namely, good and poor prognosis. Findings: The best results were obtained by support vector machine with linear kernel. This classifier achieved a sensitivity and specificity of 84% and 82%, respectively, for metastasis prediction. Conclusion: Our findings provide a strategy to specify patients who would benefit from adjuvant therapy. © 2014, Isfahan University of Medical Sciences(IUMS). All rights reserved.
Related Docs
View other Related Docs
1. Evaluation of Different Classification Models to Extract Gene Signatures for Breast Cancer Recurrence Using Microarray Data, Journal of Isfahan Medical School (2017)
2. Using Protein Interaction Database and Support Vector Machines to Improve Gene Signatures for Prediction of Breast Cancer Recurrence, Journal of Medical Signals and Sensors (2013)
3. Hybrid Method for Prediction of Metastasis in Breast Cancer Patients Using Gene Expression Signals, Journal of Medical Signals and Sensors (2013)
4. Identification of Effective Factors in Breast Cancer Survival in Isfahan Using Machine Learning Techniques, Journal of Advances in Medical and Biomedical Research (2024)
5. A Hybrid Computer-Aided-Diagnosis System for Prediction of Breast Cancer Recurrence (Hpbcr) Using Optimized Ensemble Learning, Computational and Structural Biotechnology Journal (2017)
6. Prediction of Blood Cancer Using Leukemia Gene Expression Data and Sparsity-Based Gene Selection Methods, Iranian Journal of Pediatric Hematology and Oncology (2023)
7. Identification of Potential Vascular Endothelial Growth Factor Receptor Inhibitors Via Tree-Based Learning Modeling and Molecular Docking Simulation, Journal of Chemometrics (2024)
8. Identification of Gene Signatures for Classifying of Breast Cancer Subtypes Using Protein Interaction Database and Support Vector Machines, 2015 5th International Conference on Computer and Knowledge Engineering, ICCKE 2015 (2015)
Experts (# of related papers)
View all Related Experts
Mohamadreza Sehhati (12)
Alireza Mehri-Dehnavi (5)
Other Related Docs
9. Towards a Better Diagnosis of Prostate Cancer: Application of Machine Learning Algorithms, Frontiers in Health Informatics (2022)
10. Diagnosis of Coronary Artery Disease Based on Machine Learning Algorithms Support Vector Machine, Artificial Neural Network, and Random Forest, Advanced Biomedical Research (2023)
11. Ne-Nu-Svc: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease, IEEE Access (2019)
12. Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data, Iranian Journal of Radiology (2015)
13. The Performance of the Nottingham Prognosis Index and the Adjuvant Online Decision Making Tool for Prognosis in Early-Stage Breast Cancer Patients, International Journal of Preventive Medicine (2015)
14. Notice of Removal: Malignant Tumor Detection Using Linear Support Vector Machine in Breast Cancer Based on New Optimization Algorithms, Proceedings - 2012 International Symposium on Instrumentation and Measurement, Sensor Network and Automation, IMSNA 2012 (2012)
15. Using Data Mining Techniques for Predicting the Survival Rate of Breast Cancer Patients: A Review Article, Tehran University Medical Journal (2021)
16. A Novel Feature Ranking Method for Prediction of Cancer Stages Using Proteomics Data, PLoS ONE (2017)
17. A Modified Multiple-Criteria Decision-Making Approach Based on a Protein-Protein Interaction Network to Diagnose Latent Tuberculosis, BMC Medical Informatics and Decision Making (2024)
18. Classification of Prostate Cancerous Tissues by Support Vector Machine Algorithm With Different Kernels From T2-Weighted Magnetic Resonance Images, Journal of Isfahan Medical School (2021)
19. Using Hidden Markov Model to Predict Recurrence of Breast Cancer Based on Sequential Patterns in Gene Expression Profiles, Journal of Biomedical Informatics (2020)
20. An Optimized Framework for Cancer Prediction Using Immunosignature, Journal of Medical Signals and Sensors (2018)
21. An Ensemble Soft Weighted Gene Selection-Based Approach and Cancer Classification Using Modified Metaheuristic Learning, Journal of Computational Design and Engineering (2021)
22. Identifying the Most Important Factors in Determining the Osteoporosis in Women Using Data Mining Techniques, Acta Medica Iranica (2023)
23. Using Classification and K-Means Methods to Predict Breast Cancer Recurrence in Gene Expression Data, Journal of Medical Signals and Sensors (2022)
24. Diagnostic Performance Evaluation of Multiparametric Magnetic Resonance Imaging in the Detection of Prostate Cancer With Supervised Machine Learning Methods, Diagnostics (2023)
25. A Novel Feature Selection Method for Microarray Data Classification Based on Hidden Markov Model, Journal of Biomedical Informatics (2019)
26. Stable Gene Signature Selection for Prediction of Breast Cancer Recurrence Using Joint Mutual Information, IEEE/ACM Transactions on Computational Biology and Bioinformatics (2015)
27. Diagnosis of Cervical Cancer Using Texture and Morphological Features in Pap Smear Images, Journal of Isfahan Medical School (2020)
28. Identification of Disease-Causing Genes Using Microarray Data Mining and Gene Ontology, BMC Medical Genomics (2011)