Predicting breast cancer survivability: a comparison of three data mining methods

By Dursun Delen, Glenn Walker, Amit Kadam. 2005

1,252 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cancer Research, Oncology

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Citations in the corpus

Applications of Machine Learning in Cancer Prediction and Prognosis (2006, methodology): “One of the challenges in using ANNs is mapping how the real-world input/output (an image, a physical characteristic, a list of gene names, a prognosis) can be mapped to a numeric vector.”
Machine Learning for Survival Analysis (2019, background): “However, this will provide a suboptimal model because it neglects the information available in the censored instances (Delen et al. 2005; Burke et al. 1997).”
Machine Learning for Survival Analysis: A Survey (2017, background): “However, it will provide a sub-optimal model because of neglecting the available information in those cen-XXX sored instances [Delen et al. 2005; Burke et al. 1997].”
Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review (2019, background): “The development of diverse AI techniques also contributes to the prediction of breast cancer recurrence [6].”
Artificial intelligence and machine learning in precision and genomic medicine (2021, background): “Non-Omics Clinicopathological Neural Networks, Decision Tree, Logistic Regression Breast Cancer [122]”
Machine learning applications in cancer prognosis and prediction (2014, background): “Based on biological knowledge, the authors selected gene pairs that indicate strong biological interactions.”
Using methods from the data mining and machine learning literature for disease classification and prediction: A case study examining classification of heart failure sub-types (2013, result): “Although these two studies focused on disease incidence, a third study compared three methods for predicting survival in patients with breast cancer [36].”
Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature (2011, background): “For further explanation of these stages, the reader is referred to Altman & Haldeman, (1995).”
Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer Patients (2015, methodology): “The folds are stratified in a manner that they contain approximately the same proportion of predictor labels as the original dataset [12] and [14].”
Data Mining in Healthcare and Biomedicine: A Survey of the Literature (2012, methodology): “Delen andcolleaguesbuilt prediction models usingartificial neural networks, decision trees (C5.0), and logistic regression to predict breast cancer survivability [ 33 ].”
An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer (2016, methodology): “Most commonly, these prediction models are trained on individual patient records and used to predict the clinical outcome of an unseen patient, often yielding impressive out-of-sample predictions (Burke 1997, Delen et al. 2005, Lee et al. 2003, Hurria et al. 2011, Jefferson et al. 1997).”