Abstract
<p><strong>BACKGROUND</strong><strong>: </strong>Breast cancer is the main cause of women cancer mortality. Therefore, precise prediction of patients’ risk level is the major concern in therapeutic strategies. Although statistical learning algorithms are high quality risk prediction methods, but usually their better prediction quality leads to more loss of interpretability. Therefore, the aim of this study is to compare ‘Model-Based Recursive Partitioning’ and ‘Random Survival Forest’; whether the partitioning, as the more interpretable learning technique, could be a suitable successor for forests.</p><p><strong>PATIENTS &amp; METHODS</strong><strong>: </strong>The applied dataset for this retrospective cohort study includes the information of 539 Iranian females with breast cancer. To model the patients’ survival, various learning algorithms were fitted and their accuracy measures were statistically compared by means of several precision criteria.</p><p><strong>RESULTS</strong><strong>:</strong> This study verified the stability of ‘Model-based Recursive Partitioning’, further to ‘Random Survival Forest’ deficiency to present a unique pervasive model. Moreover, except ‘Log-Logistic-Based Recursive Partitioning’, none of the methods significantly outperformed ‘Exponential- Based Recursive Partitioning’.</p><p><strong>CONCLUSIONS:</strong> Briefly, it was concluded that the loss of interpretability due to the use of over complex models, may not always counterbalanced by the amount of prediction improvements.</p>
Highlights
Breast cancer is the main cause of women cancer mortality
Statistical learning algorithms are high quality risk prediction methods, but usually their better prediction quality leads to more loss of interpretability
Briefly, it was concluded that the loss of interpretability due to the use of over complex models, may not always counterbalanced by the amount of prediction improvements
Summary
Breast cancer is the main cause of women cancer mortality. precise prediction of patients’ risk level is the major concern in therapeutic strategies. ‘Model-Based Recursive Partitioning’ (MoBRP) could be referred as an exemplary interpretable machine learning technique (Zeileis, Hothorn, & Hornik, 2008a). This partitioning is a hybrid tree such that combines the traditional parametric survival models with newly introduced recursive partitioning methods (Zeileis et al, 2008a). By this way, MoBRP gains the profits of both classical and modern analysis; the prominent interpretability for recognized affective risk factors, in addition to its accurate survival time prediction www.ccsenet.org/gjhs
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