A Comparative Study between Time Series and Machine Learning Technique to Predict Dengue Fever in Dhaka City

Table 1

Some related papers on dengue fever and their output result.

Title	Work type	Best algorithm name	Outcome
Bangladesh’s Dhaka an outbreak of dengue	Analysis of statistics and a case study	Analysis of statistics	August 2019 report from the CDC
Dengue in Bangladesh’s capital city of Dhaka	Statistical evaluation	Statistical evaluation	KAP ratings of 69.2%, 71.4%, and 52%
The temporal trends of dengue fever and related meteorological factors in Bangkok	Analysis of time series and ARIMA models	The models of ARIMA	Ultimately, the correlation coefficient, MAE, RMSE, and MAPE ARIMA values were 0.90, 3.83, 6.49, and 26.45, respectively
A dengue fever forecasting model grounded in South China	Analyzing time series data and statistics	Analysis of time series	Compared to GAM (RMSE: 34.1), GAMM (RMSE: 121.9) provides a better prediction of DF cases
Results of a hospital-based study on dengue fever in Malaysia	The models of ARIMA	The models of ARIMA	(95% CI: 1.003, 1.01), RR = 1.006
Dengue fever clusters in space and time in India	Time series data evaluation	Time series data evaluation	50% of the cluster size
Our work	Time series analysis and neural network (NN)	Neural network (NN)	The NN model gives a better prediction performance with the lowest value of RMSE 7.588889e − 06 than the time series analysis