ArticleJournal of public health research2020
A five-year (2015 to 2019) analysis of studies focused on breast cancer prediction using machine learning: A systematic review and bibliometric analysis.
Article in Journal of public health research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis 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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Bibliometric analysis of research on artificial İntelligence applications in breast cancer diagnosis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2026Article
- Monte Carlo Tree Search for optimal cancer intervention strategies among BRCA mutation carriers.Computers in biology and medicine · 2025Article
- Mapping the future of early breast cancer diagnosis: a bibliometric analysis of AI innovations.Discover oncology · 2025Article
- Frontiers and hotspots of 3D technology in prostatectomy from 1999 to 2024: a bibliometric analysis and visualization.Gland surgery · 2025Article
- An approach for classification of breast cancer using lightweight deep convolution neural network.Heliyon · 2024Article
- Research Trends of Artificial Intelligence in Lung Cancer: A Combined Approach of Analysis With Latent Dirichlet Allocation and HJ-Biplot Statistical Methods.Pulmonary medicine · 2024Article
- Delineating intra-tumoral heterogeneity and tumor evolution in breast cancer using precision-based approaches.Frontiers in genetics · 2023Review
- Go-Ichi-Ni-San 2: A potential biomarker and therapeutic target in human cancers.World journal of gastrointestinal oncology · 2022Review
- Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews.International journal of medical informatics · 2022Review
- Bibliometric analysis of the published literature on machine learning in economics and econometrics.Social network analysis and mining · 2022Article
- Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis.Healthcare (Basel, Switzerland) · 2021Article
- Machine Learning Models to Improve the Differentiation Between Benign and Malignant Breast Lesions on Ultrasound: A Multicenter External Validation Study.Cancer management and research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective 1 of this study was to investigate trends in breast cancer (BC) prediction using machine learning (ML) publications by analysing country, first author, journal, institutional collaborations and co-occurrence of author keywords. The objective 2 was to provide a review of studies on BC prediction using ML and a blood analysis dataset (Breast Cancer Coimbra Dataset [BCCD]), and the objective 3 was to provide a brief review of studies based on BC prediction using ML and patients' fine needle aspirate cytology data (Wisconsin Breast Cancer Dataset [WBCD]). The design of this study was as follows: for objective 1: bibliometric analysis, data source PubMed (2015-2019); for objective 2: systematic review, data source: Google and Google Scholar (2018-2019); for objective 3: systematic review, data source: Google Scholar (2016-2019). The inclusion criteria for objective 1 were all publication results yielded from the searches. All English papers that had a 'PDF' option from the search results were included for objective 2. A sample of the 'PDF' English papers were included for objective 3. All 116 female patients from the BCCD, consisting of 64 positive BC patients and 52 controls were included in the study for objective 2. For the WBCD, all 699 female patients comprising of 458 with a benign BC tumour and 241 with a malignant BC tumour were included for objective 3. All 2928 publications were included for objective 1. The results showed that the United States of America (USA) produced the highest number of publications (n=803). In total, 2419 first authors contributed towards the publications. Breast Cancer Research and Treatment was the highest ranked journal. Institutional collaborations mainly occurred within the USA. The use of ML for BC screening and detection was the most researched topic. A total of 19 distinct papers were included for objectives 2 and 3. The findings from these studies were never presented to clinicians for validations. In conclusion, the use of ML for BC screening and detection is promising.
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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.