Identification of Pulmonary Hypertension Animal Models Using a New Evolutionary Machine Learning Framework Based on Blood Routine Indicators

Hu, Jiao; Lv, Shushu; Zhou, Tao; Chen, Huiling; Liu, Xiao; Huang, Xiaoying; Wang, Liangxing; Wu, Peiliang

doi:10.1007/s42235-022-00292-z

Cited by 9 publications

(1 citation statement)

References 104 publications

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“…Therefore, the SGHHO method can also be combined with the latest optimization algorithms in future research, such as the farmland fertility algorithm, hunger games search, etc. And furthermore, it is applied in areas such as financial risk prediction and medical data diagnosis [ 135 ]. Nowadays, along with the increasing size of data in various fields, large-scale datasets also generate a large amount of redundant, useless and noisy data.…”

Section: Discussionmentioning

confidence: 99%

An enhanced decision-making framework for predicting future trends of sharing economy

Wu,

Tang,

et al. 2023

PLoS ONE

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This work aims to provide a reliable and intelligent prediction model for future trends in sharing economy. Moreover, it presents valuable insights for decision-making and policy development by relevant governmental bodies. Furthermore, the study introduces a predictive system that incorporates an enhanced Harris Hawk Optimization (HHO) algorithm and a K-Nearest Neighbor (KNN) forecasting framework. The method utilizes an improved simulated annealing mechanism and a Gaussian bare bone structure to improve the original HHO, termed SGHHO. To achieve optimal prediction performance and identify essential features, a refined simulated annealing mechanism is employed to mitigate the susceptibility of the original HHO algorithm to local optima. The algorithm employs a mechanism that boosts its global search ability by generating fresh solution sets at a specific likelihood. This mechanism dynamically adjusts the equilibrium between the exploration and exploitation phases, incorporating the Gaussian bare bone strategy. The best classification model (SGHHO-KNN) is developed to mine the key features with the improvement of both strategies. To assess the exceptional efficacy of the SGHHO algorithm, this investigation conducted a series of comparative trials employing the function set of IEEE CEC 2014. The outcomes of these experiments unequivocally demonstrate that the SGHHO algorithm outperforms the original HHO algorithm on 96.7% of the functions, substantiating its remarkable superiority. The algorithm can achieve the optimal value of the function on 67% of the tested functions and significantly outperforms other competing algorithms. In addition, the key features selected by the SGHHO-KNN model in the prediction experiment, including " Form of sharing economy in your region " and " Attitudes to the sharing economy ", are important for predicting the future trends of the sharing economy in this study. The results of the prediction demonstrate that the proposed model achieves an accuracy rate of 99.70% and a specificity rate of 99.38%. Consequently, the SGHHO-KNN model holds great potential as a reliable tool for forecasting the forthcoming trajectory of the sharing economy.

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Section: Discussionmentioning

confidence: 99%