Content-Based Health Recommender System for ICU Patient

Neloy, Asif Ahmed; Oshman, Muhammad Shafayat; Islam, Md. Monzurul; Hossain, Md. Julhas; Zahir, Zunayeed Bin

doi:10.1007/978-3-030-33709-4_20

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…When n is large the brute kNN is not a practicable method. Many improved methods are designed to solve this problem such as the kd tree [17], [18], ball tree [19], [20], Hybrid Spill Tree [25], [26] and so on. The time complexity of these tree-based methods are O(log(n)).…”

Section: B Kd Tree and Ball Treementioning

confidence: 99%

“…Because kd tree resides in memory after it is trained, it can also be used in service layer to provide querying service. Compared to other kNN(K Nearest Neighbor) related algorithms such as brute kNN and ball tree [19], [20], kd tree has its advantages in performance. Other models such as linear regression, neural network, SVM and so on can also be used in in batch and realtime layers.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Price of Second-Hand Housing Based on Lambda Architecture and KD Tree

Pan¹,

Qiu²,

Xu³

et al. 2022

Infocommunications journal

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In this paper a system is designed and implemented to predict the price of second-hand housing. This system based on Lambda architecture can execute prediction in both real-time and batch modes so it can give two kinds of different price predictions that reflect current and historical conditions respectively. The kNN related algorithms are used for price prediction. By comparing the performance of brute kNN, kd tree and ball tree, kd tree is selected as the price prediction model of the system. In system implementation the kd tree model is chosen to predict prices in both real-time and batch services. The kd tree model can also recommend housings to user besides price prediction. The experiment shows the effectiveness of our system. Time and space performance of brute kNN, kd tree and ball tree are compared by experiments. And the evaluation metrics of other available maching learning models are compared. The reason of choosing the kd tree model is also explained by the experimental results.

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