Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6891138 | Computer Methods and Programs in Biomedicine | 2018 | 24 Pages |
Abstract
A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the survivability of patient. The results of the analysis can be used to segment the historical patient data into clusters or subsets, which share common variable values and survivability. The survivability prediction accuracy of a MLP is improved by using identified patient cohorts as opposed to using raw historical data. Analysis of variable values in each cohort provide better insights into survivability of a particular subgroup of breast cancer patients.
Keywords
ANNRNNDBSCANTNMDLBCLAJCCMARSSLMLPRBFNaïve BayesSOMSurveillance, Epidemiology, and End Results Programtumor, node, metastasisDecision treesSEERRecurrent neural networkartificial neural networksRadial basis functionDiffuse large B cell lymphomaSVMSupport vector machineSelf-organising mapMulti-Layer PerceptronAmerican Joint Committee on CancerMachine learningSemi-supervised learning
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Nagesh Shukla, Markus Hagenbuchner, Khin Than Win, Jack Yang,