Up Sampling Data in Bagging Tree Classification and Regression Decision Tree Method for Dengue Shock Syndrome Detection

Author:

Muflikhah Lailil,Iskandar Agustin,Yudistira Novanto,Dewanto Bambang Nur,Nadhori Isbat Uzzin,Nisa Lisa Khoirun

Publisher

Springer Nature Singapore

Reference18 articles.

1. Sarma, D., Hossain, S., Mittra, T., Bhuiya, Md.A.M., Saha, I., Chakma, R.: Dengue prediction using machine learning algorithms. In: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC), pp. 1–6 (2020). https://doi.org/10.1109/R10-HTC49770.2020.9357035

2. Yuan, K., Chen, Y., Zhong, M., Lin, Y., Liu, L.: Risk and predictive factors for severe dengue infection: a systematic review and meta-analysis. PLoS ONE 17(4), e0267186 (2022)

3. World Health Organisation. Dengue and severe dengue. WHO Fact Sheet [Internet] 117, 1–4 (2014). Tersedia pada: https://www.who.int/mediacentre/factsheets/fs117/en/index.html

4. WHO | Epidemiology [Internet].  https://www.who.int/denguecontrol/epidemiology/en/ Accessed 16 Sept 2020

5. Marimuthu, T., Balamurugan, V.: A novel bio-computational model for mining the dengue gene sequences. In 2015.  https://www.semanticscholar.org/paper/A-NOVEL-BIO-COMPUTATIONAL-MODEL-FOR-MINING-THE-GENE-Marimuthu-Balamurugan/aa6c9fd853eeb246e49355f00050f55e4f501cfc. Accessed 12 Apr 2023 

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1. Forecasting Machine Learning Based Feature Selection for Dengue Prediction in the Early Stage;2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC);2023-12-04

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