While machine learning has advanced in medicine, its widespread use in clinical applications, especially in predicting breast cancer metastasis, is still limited. We have been dedicated to constructing a DFNN model to predict breast cancer metastasis n years in advance. However, the challenge lies in efficiently identifying optimal hyperparameter values through grid search, given the constraints of time and resources. Issues such as the infinite possibilities for continuous hyperparameters like l1 and l2, as well as the time-consuming and costly process, further complicate the task. To address these challenges, we developed Single Hyperparameter Grid Search (SHGS) strategy, serving as a preselection method before grid search. Our experiments with SHGS applied to DFNN models for breast cancer metastasis prediction focus on analyzing eight target hyperparameters: epochs, batch size, dropout, L1, L2, learning rate, decay, and momentum. We created three figures, each depicting the experiment results obtained from three LSM-I-10-Plus-year datasets. These figures illustrate the relationship between model performance and the target hyperparameter values. For each hyperparameter, we analyzed whether changes in this hyperparameter would affect model performance, examined if there were specific patterns, and explored how to choose values for the particular hyperparameter. Our experimental findings reveal that the optimal value of a hyperparameter is not only dependent on the dataset but is also significantly influenced by the settings of other hyperparameters. Additionally, our experiments suggested some reduced range of values for a target hyperparameter, which may be helpful for low-budget grid search. This approach serves as a prior experience and foundation for subsequent use of grid search to enhance model performance.

翻译：尽管机器学习在医学领域取得了进展，但其在临床应用中，尤其是在预测乳腺癌转移方面的广泛使用仍然有限。我们一直致力于构建一个深度前馈神经网络模型，以提前n年预测乳腺癌转移。然而，挑战在于如何在时间和资源有限的情况下，通过网格搜索高效地确定最优超参数值。诸如l1和l2等连续超参数的无限可能性，以及耗时且成本高昂的过程，进一步使任务复杂化。为应对这些挑战，我们开发了单超参数网格搜索策略，作为网格搜索前的预选方法。我们将SHGS应用于乳腺癌转移预测的DFNN模型进行实验，重点分析了八个目标超参数：训练轮数、批次大小、丢弃率、L1正则化、L2正则化、学习率、衰减率和动量。我们创建了三张图，每张图展示了基于三个LSM-I-10-Plus年数据集获得的实验结果。这些图说明了模型性能与目标超参数值之间的关系。针对每个超参数，我们分析了该超参数的变化是否会影响模型性能，检验是否存在特定模式，并探讨了如何为该特定超参数选择值。我们的实验结果表明，超参数的最优值不仅取决于数据集，还显著受其他超参数设置的影响。此外，我们的实验为目标超参数提出了一些缩减的取值范围，这可能有助于低预算的网格搜索。此方法为后续使用网格搜索提升模型性能提供了先验经验和基础。