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Class imbalance problems widely exist in the medical field and heavilydeteriorates performance of clinical predictive models. Most techniques toalleviate the problem rebalance class proportions and they predominantly assumethe rebalanced proportions should be a function of the original data andoblivious to the model one uses. This work challenges this prevailingassumption and proposes that links the optimal class proportions to the modelcomplexity, thereby tuning the class proportions per model. Our experiments onthe opioid overdose prediction problem highlight the performance gain of tuningclass proportions. Rigorous regression analysis also confirms the advantages ofthe theoretical framework proposed and the statistically significantcorrelation between the hyperparameters controlling the model complexity andthe optimal class proportions.

Enhancing Clinical Predictive Modeling through Model Complexity-Driven  Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on  Opioid Overdose Prediction