In predictive healthcare data analytics, high accuracy is both vital and paramount as low accuracy can lead to misdiagnosis, which is known to cause serious health consequences or death. Fast prediction is also considered an important desideratum particularly for machines and mobile devices with limited memory and processing power. For real-time health care analytics applications, particularly the ones that run on mobile devices, such traits (high accuracy and fast prediction) are highly desirable. In this paper, we propose to use an ensemble regression technique based on CLUB-DRF , which is a pruned Random Forest that possesses these features. The speed and accuracy of the method have been demonstrated by an experimental study on three medical data sets of three different diseases.

中文翻译：

---

医疗保健数据分析中资源高效的快速预测：修剪的随机森林回归方法

在预测性医疗保健数据分析中，高精度既至关重要又至关重要，因为低精度会导致误诊，众所周知，误诊会导致严重的健康后果或死亡。快速预测也被认为是一个重要的需求，特别是对于内存和处理能力有限的机器和移动设备。对于实时医疗保健分析应用程序，尤其是在移动设备上运行的应用程序，此类特征（高精度和快速预测）是非常需要的。在本文中，我们建议使用基于 CLUB-DRF 的集成回归技术，CLUB-DRF 是具有这些特征的修剪随机森林。对三种不同疾病的三个医学数据集的实验研究证明了该方法的速度和准确性。