Using the Health Care Cost Institute data (approximately 47 million members over seven years), we examine how to best predict which members will be high-cost next year. We find that cost history, age, and prescription drug coverage all predict high costs, with cost history being by far the most predictive. We also compare the predictive accuracy of logistic regression to extreme gradient boosting (XGBoost) and find that the added flexibility of the extreme gradient boosting improves the predictive power. Finally, we show that with extremely unbalanced classes (because high-cost members are so rare), oversampling the minority class provides a better XGBoost predictive model than undersampling the majority class or using the training data as is. Logistic regression performance seems unaffected by the method of sampling.

使用卫生保健成本研究所的数据（七年中约有4,700万会员），我们研究了如何最好地预测明年哪些会员将是高成本的。我们发现成本历史，年龄和处方药覆盖率均预测高成本，而成本历史迄今为止最具预测性。我们还比较了逻辑回归与极限梯度增强（XGBoost）的预测准确性，发现极限梯度增强的附加灵活性提高了预测能力。最后，我们表明，在班级极不平衡的情况下（因为高成本的成员很少见），与对大多数班级进行过低抽样或按原样使用培训数据相比，对少数派进行过度抽样提供了更好的XGBoost预测模型。Logistic回归性能似乎不受抽样方法的影响。