@Article{cmc.2022.021747,
AUTHOR = {Shobhit Verma
, Nonita Sharma
, Aman Singh
, Abdullah Alharbi
, Wael Alosaimi
, Hashem Alyami,
Deepali Gupta, Nitin Goyal},
TITLE = {An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {70},
YEAR = {2022},
NUMBER = {3},
PAGES = {6041--6055},
URL = {http://www.techscience.com/cmc/v70n3/45032},
ISSN = {1546-2226},
ABSTRACT = {This research work proposes a new stack-based generalization
ensemble model to forecast the number of incidences of conjunctivitis disease.
In addition to forecasting the occurrences of conjunctivitis incidences, the
proposed model also improves performance by using the ensemble model.
Weekly rate of acute Conjunctivitis per 1000 for Hong Kong is collected for
the duration of the first week of January 2010 to the last week of December
2019. Pre-processing techniques such as imputation of missing values and
logarithmic transformation are applied to pre-process the data sets. A stacked
generalization ensemble model based on Auto-ARIMA (Autoregressive Integrated Moving Average), NNAR (Neural Network Autoregression), ETS
(Exponential Smoothing), HW (Holt Winter) is proposed and applied on
the dataset. Predictive analysis is conducted on the collected dataset of conjunctivitis disease, and further compared for different performance measures.
The result shows that the RMSE (Root Mean Square Error), MAE (Mean
Absolute Error), MAPE (Mean Absolute Percentage Error), ACF1 (Auto
Correlation Function) of the proposed ensemble is decreased significantly.
Considering the RMSE, for instance, error values are reduced by 39.23%,
9.13%, 20.42%, and 17.13% in comparison to Auto-ARIMA, NAR, ETS,
and HW model respectively. This research concludes that the accuracy of the
forecasting of diseases can be significantly increased by applying the proposed
stack generalization ensemble model as it minimizes the prediction error and
hence provides better prediction trends as compared to Auto-ARIMA, NAR,
ETS, and HW model applied discretely},
DOI = {10.32604/cmc.2022.021747}
}