Controlling disease risk (diagnostic errors) poses a significant challenge in the field of medicine, largely due to the inherent data imbalance between affected (positive) and healthy (negative) individuals. To address this challenge, we propose an effective classification strategy tailored for disease risk control. Our research aims to regulate the false negative rate (type I errors) while minimizing the false positive rate (type II errors). We employ the Neyman-Pearson paradigm, combining oversampling and feature selection techniques to balance the training dataset, thereby improving disease risk prediction accuracy. To evaluate our approach, we conduct comprehensive numerical experiments on an imbalanced Heart Failure Clinical Records dataset. The results demonstrate that oversampling techniques and feature selection can enhance the accuracy of disease risk control to a certain degree, and our proposed strategy achieves robust and commendable performance.