Login Register Cart 0
Generic placeholder image

Current Pharmaceutical Biotechnology

Editor-in-Chief

ISSN (Print): 1389-2010
ISSN (Online): 1873-4316

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Letter Article

A Weighted Ensemble Model for Prediction of Infectious Diseases

Author(s): Kumar Shashvat*, Rikmantra Basu, Amol P. Bhondekar and Arshpreet Kaur

Volume 20, Issue 8, 2019

Page: [674 - 678] Pages: 5

DOI: 10.2174/1389201020666190612160631

Price: $65

Abstract

Background: The ensemble building is a common method to improve the performance of the model in case of regression as well as classification.

Objective: In this paper we propose a weighted average ensemble model to predict the number of incidence for infectious diseases like typhoid and compare it with applied models for prediction.

Methods: The Monthly data of dengue and typhoid cases from 2014 to 2017 were taken from integrated diseases surveillance programme, Government of India. The data was processed by three regressions such as support vector regression, neural network and linear regression.

Results: To evaluate the prediction error and compare it with different models, different performance measures have been used such as MSE, RMSE and MAE and it was found that proposed ensemble method performed better in terms of forecast measures.

Conclusion: Our main aim in this paper is to minimize the prediction error; the resulting proposed weighted average ensemble model has shown a significant result in terms of prediction errors.

Keywords: Ensemble, regression, prediction, typhoid, bioengineering, weighted ensemble model.

« Previous Next »
Graphical Abstract

[1]
Bu, H.; Hao, J.; Guan, J.; Zhou, S. Predicting enhancers from multiple cell lines and tissues across different developmental stages based on SVM method. Curr. Bioinform., 2018, 13(6), 655-660.
[2]
Khashei, M.; Bijari, M. An artificial neural network (p, d, q) model for time series forecasting. Expert Syst. Appl., 2010, 37(1), 479-489.
[3]
Cerqueira, V.; Torgo, L.; Pinto, F.; Soares, C. Arbitrated ensemble for time series forecasting. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics., 2018, 10535, 478-494.
[4]
Oliveira, M.; Torgo, L. Ensembles for time series forecasting., 2014.
[5]
Wang, H.; Fan, W.; Sun, F.; Qian, X. An adaptive ensemble model of extreme learning machine for time series prediction. 12th Int. Computer Conf. Wavelet Active Media Technol. Informat. Processing, ICCWAMTIP . 2015, pp. 80-85.
[6]
Mandal, B.; Puhan, N.; Verma, A. Deep convolutional generative adversarial network based food recognition using partially labeled data. IEEE Sensors Lett., 2018, 3(2), 1-4.
[7]
Sur, D.; Ali, M.Von; Seidlein, L.; Manna, B.; Deen, J.L.; Acosta, C.J.; Bhattacharya, S.K. Comparisons of predictors for typhoid and paratyphoid fever in Kolkata, India. BMC Public Health, 2007, 7, 1-10.
[8]
Bhatnagar, S.; Lal, V.; Gupta, S.D.; Gupta, O.P. Forecasting incidence of dengue in Rajasthan, using time series analyses. Indian J. Public Health, 2012, 56(4), 281-285.
[9]
Zhang, Y.; Wang, T.; Liu, K.; Xia, Y.; Lu, Y.; Jing, Q.; Yang, Z.; Hu, W.; Lu, J. Developing a time series predictive model for dengue in Zhongshan, China based on weather and Guangzhou Dengue surveillance data. PLoS Negl. Trop. Dis., 2016, 10(2)e0004473
[10]
Buczak, A.L.; Baugher, B.; Moniz, L.J.; Bagley, T.; Babin, S.M.; Guven, E. Ensemble method for dengue prediction. PLoS One, 2018, 13(1)e0189988
[11]
Yamana, T.K.; Kandula, S.; Shaman, J. Super ensemble forecasts of dengue outbreaks. J. R. Soc. Interface, 2016, 13(123)20160410
[12]
Zhang, X.; Zhang, T.; Young, A.A.; Li, X. Applications and comparisons of four-time series models in epidemiological surveillance data. PLoS One, 2014, 9(2)e88075
[13]
Dietterich, T.G. Ensemble methods in machine learning; Oregon State University Corvallis Oregon USA, 2007, pp. 1-15.
[14]
Agrawal, R.K. An introductory study on time series modeling and forecasting. arXiv Preprint arXiv:1302.6613,, 2013, 1-68.
[15]
Ramos, D.; Hervás, F.; Gámez, C. Simple and low-cost procedure for monthly and yearly stream flow forecasts during the current hydrological year. Water (Switzerland), 2014, 10(8), 2018.

Rights & Permissions Print Cite
Article Metrics
27
5
1
1
© 2024 Bentham Science Publishers | Privacy Policy