Background and objectives

To use a points system based on a logistic regression model to predict a binary event in a given population, the validation of this system is necessary. The most correct way to do this is to calculate discrimination and calibration using bootstrapping. Discrimination can be addressed through the area under the receiver operating characteristic curve (AUC) and calibration through the representation of the smoothed calibration plot (most recommended method). As this is not a simple task, we developed a methodology to construct a mobile application in Android to perform this task.

Methods

The construction of the application is based on source code written in language supported by Android. It is designed to use a database of subjects to be analyzed and to be able to apply statistical methods widely used in the scientific literature to validate a points system (bootstrap, AUC, logistic regression models and smooth curves). As an example our methodology was applied on simulated points system data (doi: 10.1111/ijcp.12851) to predict mortality on admission to intensive care units (Google Play: ICU mortality). The results were compared with those obtained applying the same methods in the R statistical package.

Results

No differences were found between the results obtained in the mobile application and those from the R statistical package, an expected result when applying the same mathematical techniques.

Conclusions

Our methodology may be applied to other point systems for predicting binary events, as well as to other types of predictive models.

中文翻译：

---

一种基于逻辑回归模型的评分系统验证方法，可通过Android的移动应用程序以实际案例为例，预测二进制结果。

背景和目标

为了使用基于逻辑回归模型的积分系统来预测给定总体中的二元事件，必须对此系统进行验证。执行此操作的最正确方法是使用自举来计算判别和校准。可以通过接收器工作特性曲线（AUC）下的区域解决歧视问题，并可以通过平滑校准图的表示法进行校准（最推荐的方法）。由于这不是一项简单的任务，我们开发了一种方法来在Android中构建移动应用程序来执行此任务。

方法

该应用程序的构建基于以Android支持的语言编写的源代码。它旨在使用要分析的对象的数据库，并能够应用科学文献中广泛使用的统计方法来验证积分系统（自举，AUC，逻辑回归模型和平滑曲线）。例如，我们的方法已应用于模拟积分系统数据（doi：10.1111 / ijcp.12851），以预测重症监护病房入院时的死亡率（Google Play：ICU死亡率）。将结果与在R统计软件包中使用相同方法获得的结果进行比较。

结果

在移动应用程序中获得的结果与R统计包中的结果之间没有发现差异，这是应用相同数学技术时的预期结果。

结论

我们的方法可以应用于预测二进制事件的其他点系统，以及其他类型的预测模型。