Hypertension Prediction Using Optimal Random Forest and Real Medical Data-Reference-Cited by-同舟云学术

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Hypertension Prediction Using Optimal Random Forest and Real Medical Data

Published:2022-12-02 Issue: Volume: Page:
ISSN:
Container-title:2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)
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Author:

Ren Lijuan¹,Seklouli Aicha Sekhari¹,Wang Tao²,Zhang Haiqing³,Bouras Abdelaziz⁴

Affiliation:

1. Decision and Information Systems for Production systems(DISP-UR4570), Univ Lyon, Univ Lyon 2, INSA Lyon, Université Claude Bernard Lyon 1,Lyon,France

2. Decision and Information Systems for Production systems(DISP-UR4570), Univ Lyon, Univ Jean Monnet Saint-Etienne, INSA Lyon, Univ Lyon 2, Université Claude Bernard Lyon 1,Roanne,France

3. School of software engineering, Chengdu University of Information Technology,Chengdu,China

4. College of Engineering, Qatar University,Doha,Qatar

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10029388/10029397/10029572.pdf?arnumber=10029572

Reference26 articles.

1. Nonparametric discrimination: consistency properties;fix;Randolph Field Texas Project,1951

2. A hybrid machine learning approach for hypertension risk prediction

3. Predicting Hypertension Based on Machine Learning Methods: A Case Study in Northwest Vietnam

4. Lightgbm: A highly efficient gradient boosting decision tree;ke;Advances in neural information processing systems,2017

5. An artificial neural network approach for predicting hypertension using NHANES data

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