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A two-stage modeling approach for breast cancer survivability prediction - 2020

Research Area:  Machine Learning

Abstract:

This paper proposes a two-stage data analytic framework, where Stage I classifies the survival and deceased statuses and Stage II predicts the number of survival months for deceased females with cancer. Since medical data are not entirely clean nor prepared for model development, we aim to show that data preparation can strengthen a simple Generalized Linear Model (GLM)1 to predict as accurate as the complex models like Extreme Gradient Boosting (XGB)2 and Multilayer Perceptron based on Artificial Neural Networks (MLP-ANNs)3 in both stages.

Author(s) Name:  ZahraSedighi-Maman,AlexaMondello

Journal name:  International Journal of Medical Informatics

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.ijmedinf.2021.104438

Volume Information:  Volume 149, May 2021, 104438