Shobhit Verma1
, Nonita Sharma1
, Aman Singh2
, Abdullah Alharbi3
, Wael Alosaimi3
, Hashem Alyami4,
Deepali Gupta5, Nitin Goyal5 ,*
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6041-6055, 2022, DOI:10.32604/cmc.2022.021747
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… More >
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