ArticleComputational and structural biotechnology journal2017
A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning.
Article in Computational and structural biotechnology journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 4 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 4 syntheses or guidelines pooled it, 139 citations in OpenAlex.
- Predictive value of machine learning for breast cancer recurrence: a systematic review and meta-analysis.Journal of cancer research and clinical oncology · 2023Pooled it
- Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning-Based Models.JCO clinical cancer informatics · 2023Pooled it
- Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
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- Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.PloS one · 2024Article
- A novel hybrid model to predict concomitant diseases for Hashimoto's thyroiditis.BMC bioinformatics · 2023Article
- Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification.Diagnostics (Basel, Switzerland) · 2023Article
- Employing Atrous Pyramid Convolutional Deep Learning Approach for Detection to Diagnose Breast Cancer Tumors.Computational intelligence and neuroscience · 2023Article
- ML-DSTnet: A Novel Hybrid Model for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning and Dempster-Shafer Theory.Computational intelligence and neuroscience · 2023Article
- Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review.Journal of personalized medicine · 2022Review
- Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis.Cancers · 2022Review
- An automated machine learning tool for breast cancer diagnosis for healthcare professionals.Health systems (Basingstoke, England) · 2022Article
- Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.Frontiers in oncology · 2022Article
- Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules.Computational and mathematical methods in medicine · 2022Article
- Neighborhood-based inference and restricted Boltzmann machine for microbe and drug associations prediction.PeerJ · 2022Article
- Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications.European radiology experimental · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT). The proper combination of selected categorical features and also the weight (importance) of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictor of breast cancer recurrence) was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as supported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selected features were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymph nodes, progesterone receptor expression, having hormone therapy and type of surgery. The minimum sensitivity, specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellent agreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, and tissue biopsy). This algorithm is thus a promising online tool for the prediction of breast cancer recurrence.
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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.