ArticleJournal of environmental and public health2022
Comparative Analysis of Machine Learning Methods for Breast Cancer Classification in Genetic Sequences.
Article in Journal of environmental and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
3 citing papers in PubMed.
- Article
- A Metabolomics-Based Study on the Discriminative Classification Models and Toxicological Mechanism of Estazolam Fatal Intoxication.Metabolites · 2023Article
- Retracted: Comparative Analysis of Machine Learning Methods for Breast Cancer Classification in Genetic Sequences.Journal of environmental and public health · 2023Article
Corrections and comments
- Retraction · 2023-12-06Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the leading cancer in women, which accounts for millions of deaths worldwide. Early and accurate detection, prognosis, cure, and prevention of breast cancer is a major challenge to society. Hence, a precise and reliable system is vital for the classification of cancerous sequences. Machine learning classifiers contribute much to the process of early prediction and diagnosis of cancer. In this paper, a comparative study of four machine learning classifiers such as random forest, decision tree, AdaBoost, and gradient boosting is implemented for the classification of a benign and malignant tumor. To derive the most efficient machine learning model, NCBI datasets are utilized. Performance evaluation is conducted, and all four classifiers are compared based on the results. The aim of the work is to derive the most efficient machine-learning model for the diagnosis of breast cancer. It was observed that gradient boosting outperformed all other models and achieved a classification accuracy of 95.82%.
Indexed as
Identifiers
What OpenQuestion holds
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.