Prediction of Dengue using Recurrent Neural Network

Chovatiya, Megha; Dhameliya, Anushka; Deokar, Jayita; Gonsalves, Jessica; Mathur, Amrita

doi:10.1109/icoei.2019.8862581

Cited by 23 publications

(14 citation statements)

References 2 publications

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“…Long Short Term Memory, usually known as LSTM, was introduced for the first time in 1997 by Hochreiter and Schmidhuber [22]. Its main function is to remember information for long periods of time [11], having their internal memory for processing sequences of inputs, by recording old and current data [23]. One of its advantages is that to address long time lag issues, LSTM can manage noise, spread patterns and constant variables, as will be used in the present study.…”

Section: Methodsmentioning

confidence: 99%

Air Quality Prediction (PM2.5 and PM10) at the Upper Hunter Town - Muswellbrook using the Long-Short-Term Memory Method

Delgado¹,

Ricardo²,

Carbajal³

2020

IJACSA

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Air quality is crucial for the environment and the life quality of citizens. Therefore, in the present study a software application is developed to predict air quality on the basis of 2.5 particulate matter (.) and 10particulate matter (), in the city of Upper Hunter, Australia, as it is considered to be one of the cities with the lowest air quality levels worldwide. For this purpose, it has been decided to use the methodology of long-short term memory (LSTM) from data collected by NSW department of planning industry and environment during the period of 30 September 2012 to 30 September 2019, to predict the behavior of the mentioned particulate matter during the month of October 2019. A comparison between the average and maximum values suggested by the software and the actual values has been made and it is shown that the predicted results of the study are quite close to reality. Finally, the results obtained in this study may serve as a basis for local authorities to proceed with the necessary protocols and measures in case an alarming prediction occurs.

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Section: Methodsmentioning

confidence: 99%

Air Quality Prediction (PM2.5 and PM10) at the Upper Hunter Town - Muswellbrook using the Long-Short-Term Memory Method

Delgado¹,

Ricardo²,

Carbajal³

2020

IJACSA

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show abstract

“…Asian countries have started to explore the use of LSTMs in forecasting dengue cases. Prediction models with LSTM have been proposed to forecast the reported number of dengue cases in Indonesia, India, and South Korea using climate variables (Chae et al 2018;Chovatiya et al 2019;Kurnianingsih et al 2020). In China, Xu and colleagues (2020) proposed an LSTM model with transfer learning to produce accurate forecasts of dengue case count using monthly dengue cases and weather data in selected cities in China with fewer dengue cases.…”

Section: Philippinementioning

confidence: 99%

“…Other hyperparameters were fixed with batch size set at 12 to reflect the yearly transmission cycle of dengue cases, and the epoch size set at 50 the same as (Chovatiya et al 2019). The adaptive momentum optimizer was used for model optimization with a learning rate set at the default rate of 0.001.…”

Section: Model Formulationmentioning

confidence: 99%

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Deep Learning Approach to Forecasting Dengue Cases in Davao City Using Long Short-term Memory (LSTM)

Ligue¹,

Ligue²

2022

Philipp J Sci

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Dengue is a mosquito-transmitted viral disease that causes mild to severe infection. In the tropics, the risk of high infection is driven by factors that influence its vector’s population density such as meteorological variables and unplanned rapid urbanization. In the Philippines, dengue remains endemic in all regions reporting hundreds of thousands of cases annually. The continuous development of data-based infectious disease prediction models plays a vital role in overcoming this persistent adversity. This study explored the application of artificial intelligence (AI) through a deep learning approach using the long short-term memory (LSTM) architecture in our prediction model. This is compared with the traditional feed-forward network approach using a multilayer perceptron (MLP) model and a statistical approach using non-seasonal and seasonal autoregressive integrated moving average (ARIMA). The forecasting models predicted the monthly number of reported dengue cases in Davao City using temperature, rainfall, relative humidity, and previous monthly cases. Model performance was evaluated using the root mean square error (RMSE). The LSTM model recorded the highest accuracy among the models, reporting two times lower RMSE than the MLP model and four times lower RMSE than the statistical models. This result demonstrated the feasibility of deep learning techniques to capture nonlinear characteristics of data and the ability of the LSTM to effectively incorporate information from longer past periods in its prediction.

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“…implement predict models [9], also for influenza, with a multi channel neural network [10]. The main part of this work is the training and validation of a LSTM on MATLAB and Colab in order to obtain a prediction model with acceptable levels of efficiency and error.…”

mentioning

confidence: 99%