SynthesisAsian Pacific journal of cancer prevention : APJCP2018
Diagnostic Accuracy of Different Machine Learning Algorithms for Breast Cancer Risk Calculation: a Meta-Analysis
Synthesis in Asian Pacific journal of cancer prevention : APJCP, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 5 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Artificial intelligence-assisted PET imaging for predicting neoadjuvant chemotherapy response in breast cancer: a systematic review and meta-analysis.European journal of nuclear medicine and molecular imaging · 2026Pooled it
- Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis.World journal of surgical oncology · 2025Pooled it
- Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning-Based Models.JCO clinical cancer informatics · 2023Pooled it
- Association of Dietary Intake Ratio of n-3/n-6 Polyunsaturated Fatty Acids with Breast Cancer Risk in Western and Asian Countries: A Meta-AnalysisAsian Pacific journal of cancer prevention : APJCP · 2019Pooled it
- Protective Effect of Omega-3 Fatty Acids in Fish Consumption Against Breast Cancer in Asian Patients: A Meta-AnalysisAsian Pacific journal of cancer prevention : APJCP · 2019Pooled it
- Genetic and lifestyle factors for breast cancer risk assessment in Southeast China.Cancer medicine · 2023Trial
- Scoring System for Predicting Breast Cancer Risk Among Women in Regions with Limited Healthcare Infrastructure in Indonesia.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Enhancing breast cancer diagnosis through machine learning algorithms.Scientific reports · 2025Article
- Clinical characteristics and prediction model of re-positive nucleic acid tests among Omicron infections by machine learning: a real-world study of 35,488 cases.BMC infectious diseases · 2024Article
- Risk prediction model for pneumothorax or pleural effusion after microwave ablation in patients with lung malignancy.Heliyon · 2024Article
- Medical Education and Artificial Intelligence: Web of Science-Based Bibliometric Analysis (2013-2022).JMIR medical education · 2024Article
- A Review of Artificial Intelligence in Breast Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Stakeholder perspectives towards diagnostic artificial intelligence: a co-produced qualitative evidence synthesis.EClinicalMedicine · 2024Review
- Artificial intelligence in the radiological diagnosis of cancer.Bioinformation · 2024Article
- Article
- A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions.Journal of personalized medicine · 2023Article
- Endometrial cancer in women with abnormal uterine bleeding: Data mining classification methods.Caspian journal of internal medicine · 2023Article
- Review
- Article
- Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The aim of this study was to determine the diagnostic accuracy of different machine learning algorithms for breast cancer risk calculation. Methods: A meta-analysis was conducted of published research articles on diagnostic test accuracy of different machine learning algorithms for breast cancer risk calculation published between January 2000 and May 2018 in the online article databases of PubMed, ProQuest and EBSCO. Paired forest plots were employed for the analysis. Numerical values for sensitivity and specificity were obtained from false negative (FN), false positive (FP), true negative (TN) and true positive (TP) rates, presented alongside graphical representations with boxes marking the values and horizontal lines showing the confidence intervals (CIs). Summary receiver operating characteristic (SROC) curves were applied to assess the performance of diagnostic tests. Data were processed using Review Manager 5.3 (RevMan 5.3). Results: A total of 1,879 articles were reviewed, of which 11 were selected for systematic review and meta-analysis. Fve algorithms for machine learning able to predict breast cancer risk were identified: Super Vector Machine (SVM); Artificial Neural Networks (ANN); Decision Tree (DT); Naive Bayes (NB); and K-Nearest Neighbor (KNN). With the SVM, the Area Under Curve (AUC) from the SROC was > 90%, therefore classified into the excellent category. Conclusion: The meta-analysis confirmed that the SVM algorithm is able to calculate breast cancer risk with better accuracy value than other machine learning algorithms.
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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.