Enhancing Clinical Predictive Modeling through Model Complexity-Driven
  Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on
  Opioid Overdose Prediction
Yinan Liu Xinyu Dong Weimin Lyu Richard N. Rosenthal Rachel Wong Tengfei Ma Fusheng Wang
Class imbalance problems widely exist in the medical field and heavily
deteriorates performance of clinical predictive models. Most techniques to
alleviate the problem rebalance class proportions and they predominantly assume
the rebalanced proportions should be a function of the original data and
oblivious to the model one uses. This work challenges this prevailing
assumption and proposes that links the optimal class proportions to the model
complexity, thereby tuning the class proportions per model. Our experiments on
the opioid overdose prediction problem highlight the performance gain of tuning
class proportions. Rigorous regression analysis also confirms the advantages of
the theoretical framework proposed and the statistically significant
correlation between the hyperparameters controlling the model complexity and
the optimal class proportions.