scholarly journals Internet Medical Privacy Disclosure Mining and Prediction Model Construction Based on Association Rules

2022 ◽  
Vol 29 (1) ◽  
Keyword(s):  
Prediction Model ◽  
Association Rules ◽  
Model Construction ◽  
Medical Privacy

Outburst prediction and influencing factors analysis based on Boruta-Apriori and BO-SVM algorithms

2021 ◽  
pp. 1-18
Author(s):  
Zhang Zixian ◽  
Liu Xuning ◽  
Li Zhixiang ◽  
Hu Hongqiang
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Influencing Factors ◽  
Association Rules ◽  
Coal And Gas Outburst ◽  
Feature Subset ◽  
Gas Outburst ◽  
Proposed Model ◽  
Optimal Feature Subset ◽  
Feature Dimension

The influencing factors of coal and gas outburst are complex, now the accuracy and efficiency of outburst prediction and are not high, in order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outbursts based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outbursts prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved, However, the feature dimension decreased significantly; The results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model, and has high stability and robustness.

Vision-Assisted Human Motion Analysis for Bed Exit Prediction Model Construction

2017 ◽  
Author(s):  
Tian-Xiang Chen ◽  
Rong-Shue Hsiao ◽  
Chun-Hao Kao ◽  
Hsin - Piao Lin ◽  
Shiann-Shiun Jeng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Motion Analysis ◽  
Human Motion ◽  
Model Construction ◽  
Human Motion Analysis

scholarly journals Power Environment Warning Prediction Model Based on Big Data Association Rules

2020 ◽  
Vol 750 ◽  
pp. 012127
Author(s):  
Dongfang Zhang ◽  
Liangliang Yu ◽  
Ou Wang ◽  
Liang Ning
Keyword(s):  
Big Data ◽  
Prediction Model ◽  
Association Rules ◽  
Data Association ◽  
Model Based

Artificial Intelligence Predicts Progress of Diabetic Kidney Disease-Novel Prediction Model Construction with Big Data Machine Learning

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 539-P ◽  
Author(s):  
MASAKI MAKINO ◽  
MASAKI ONO ◽  
TOSHINARI ITOKO ◽  
TAKAYUKI KATSUKI ◽  
AKIRA KOSEKI ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Kidney Disease ◽  
Prediction Model ◽  
Diabetic Kidney Disease ◽  
Model Construction ◽  
Diabetic Kidney

scholarly journals micro-RNA screening and prediction model construction for diagnosis of salt-sensitive essential hypertension

Medicine ◽  
2017 ◽  
Vol 96 (17) ◽  
pp. e6417 ◽  
Author(s):  
Han Qi ◽  
Zheng Liu ◽  
Bin Liu ◽  
Han Cao ◽  
Weiping Sun ◽  
...  
Keyword(s):  
Essential Hypertension ◽  
Prediction Model ◽  
Micro Rna ◽  
Model Construction

Kernel PLS based prediction model construction and simulation on theoretical cases

2015 ◽  
Vol 165 ◽  
pp. 389-394 ◽  
Author(s):  
Mingyu Wang ◽  
Guoyang Yan ◽  
Zhongyang Fei
Keyword(s):  
Prediction Model ◽  
Model Construction
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