ArticleIranian journal of public health2021
Prediction of Breast Cancer Survival by Machine Learning Methods: An Application of Multiple Imputation.
Article in Iranian journal of public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Creation of a machine learning-based model for early identification of mild cognitive impairment risk in Parkinson's disease: A PD research center-based population study.Journal of Parkinson's disease · 2026Article
- Application of machine learning in breast cancer survival prediction using a multimethod approach.Scientific reports · 2024Article
- Positive-unlabeled learning to infer protection status and identify correlates in vaccine efficacy field trials.iScience · 2024Article
- Identification and immune features of cuproptosis-related molecular clusters in polycystic ovary syndrome.Scientific reports · 2023Article
- Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models.Frontiers in oncology · 2023Article
- Prediction of Breast Cancer using Machine Learning Approaches.Journal of biomedical physics & engineering · 2022Article
- Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.Frontiers in oncology · 2022Article
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4 authors.
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Abstract
backgroundThe low breast cancer survival rates in less developed countries are critical. The machine learning techniques predict cancers survival with high accuracy. Missing data are the most important limitation for using the highest potential of these techniques to predict cancers survival. Multiple imputation (MI) was implemented and analyzed in detail to impute the missing data of a breast cancer dataset.
methodsThe dataset was from The Omid Treatment and Research Center Urmia, Iran between Jan 2006 and Dec 2012 and had information from 856 women. The algorithms such as C5 and repeated incremental pruning to produce error reduction were applied on the imputed versions of the original dataset and the non-imputed dataset to predict and extract clinical rules, respectively.
resultsThe findings showed the performance of C5 in all the evaluation criteria including accuracy (84.42%), sensitivity (92.21%), specificity (64%), Kappa statistic (59.06%), and the area under the receiver operator characteristic (ROC) curve (0.84), was improved after imputation.
conclusionThe dataset of the present study met the requirements for using the multiple imputation method. The extracted rules after the application of MI were more comprehensive and contained knowledge that is more clinical. However, the clinical value of the extracted rules after filling in the missing data did not noticeably increase.
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