Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach

Abstract

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.

eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning

“…As demonstrated in the next subsection, the performance of eGAP will not be compared with parentRF only, but also with a random forest of identical size termed RF, where the trees in the forest are chosen at random from the parentRF, and with CLUB-DRF. The latter refers to the CLUB-DRF method used in [8,9] where clustering was the main technique used in the extreme pruning of random forests. Doing such a comparison between eGAP and CLUB-DRF can shed the light as to whether RD has the potential of improving the performance or not.…”

“…Typically, shorter trees tended to overfit the data less, and hence had a better performance. Furthermore, in this table, when comparing the performance of eGAP with the CLUB-DRF method [8,9], which only utilised clustering, we saw that RD proved to be effective in improving the performance, since eGAP was able to outperform CLUB-DRF on seven datasets. For the three datasets where CLUB-DRF was superior over eGAP, the outperformance was a very small negligible fraction of less than 1%.…”

“…Clustering was the main technique used in [8,9] to extreme pruning of random forests, and in [26], replicator dynamics was employed on a diversified random forest with subforests produced by randomised subspaces [27], to evolve subforests by allowing those with better performance to grow and those with lower performance to shrink. The use of replicator dynamics allowed subspaces of features that interact better for accurate classification to have more trees, and those subspaces that have features that are not interacting well for classification, in comparison to other subspaces, to have fewer trees.…”

“…Consequently, many redundant trees are eliminated yielding a much smaller ensemble than the original one. This technique has been used in two of our previously published articles [8,9], and showed notable boost in predictive performance metrics in both classification and regression.…”

Situation-aware recommendation system for personalized healthcare applications

“…The regression technique based on CLUBDRF is implemented by Fawagreh and Gaber (2020) in which it increases the memory capacity. This work introduces a fast prediction method that is evaluated by developing a random forest algorithm in which it detects the features of the data.…”