Using deep ensemble for influenza-like illness consultation rate prediction

Kristiani, Endah; Chen, Yuan-An; Yang, Chao-Tung; Huang, Chin‐Yin; Tsan, Yu‐Tse; Chan, Wei-Cheng

doi:10.1016/j.future.2020.12.004

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…The cases account for 29% from lung cancer, 17% from an acute lower respiratory infection, 24% from stroke, 25% from ischaemic heart disease, and 43% from chronic obstructive pulmonary disease. Therefore, the study of modeling the correlation of air pollutants and diseases such as Influenza-like Illness (ILI) and respiratory illness is notable [4].…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA

Tsan

Chen

Liu

et al. 2022

IJERPH

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This paper proposed the forecasting model of Influenza-like Illness (ILI) and respiratory disease. The dataset was extracted from the Taiwan Environmental Protection Administration (EPA) for air pollutants data and the Centers for Disease Control (CDC) for disease cases from 2009 to 2018. First, this paper applied the ARIMA method, which trained based on the weekly number of disease cases in time series. Second, we implemented the Long short-term memory (LSTM) method, which trained based on the correlation between the weekly number of diseases and air pollutants. The models were also trained and evaluated based on five and ten years of historical data. Autoregressive integrated moving average (ARIMA) has an excellent model in the five-year dataset of ILI at 2564.9 compared to ten years at 8173.6 of RMSE value. This accuracy is similar to the Respiratory dataset, which gets 15,656.7 in the five-year dataset and 22,680.4 of RMSE value in the ten-year dataset. On the contrary, LSTM has better accuracy in the ten-year dataset than the five-year dataset. For example, on average of RMSE in the ILI dataset, LSTM has 720.2 RMSE value in five years and 517.0 in ten years dataset. Also, in the Respiratory disease dataset, LSTM gets 4768.6 of five years of data and 3254.3 of the ten-year dataset. These experiments revealed that the LSTM model generally outperforms ARIMA by three to seven times higher model performance.

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Section: Introductionmentioning

confidence: 99%

The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA

Tsan

Chen

Liu

et al. 2022

IJERPH

Self Cite

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“…These approaches, on the other hand, are mostly based on trial-and-error scenarios, which necessitates the training of various models multiple times in order to identify the best among them. Next, we train the dataset using LSTM and use the models [ 40 , 41 , 42 ]. Figure 1 shows the workflows of this research.…”

Section: Methodsmentioning

confidence: 99%

In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning

Tsan

Kristiani

Liu

et al. 2022

IJERPH

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The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease impacts economic, political, and cultural sectors, which causes social implications. Across the world, strategies aimed at quickly recognizing risk factors have also helped shape public health guidelines and direct resources; however, they are challenging to analyze and predict since those events still happen. This paper intends to invesitgate the association between air pollutants and COVID-19 confirmed cases using Deep Learning. We used Delhi, India, for daily confirmed cases and air pollutant data for the dataset. We used LSTM deep learning for training the combination of COVID-19 Confirmed Case and AQI parameters over the four different lag times of 1, 3, 7, and 14 days. The finding indicates that CO is the most excellent model compared with the others, having on average, 13 RMSE values. This was followed by pressure at 15, PM2.5 at 20, NO2 at 20, and O3 at 22 error rates.

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“…Among them, various convolution kernels in the convolution layer excite the local area information of the bottom layer to a higher level through the local receptive field and obtain a more abstract feature expression through layer-by-layer transmission [ 11 ]. Its success has caused a boom in the application of convolutional neural networks, and the research of convolutional neural networks in the fields of speech recognition, target detection, semantic segmentation, and face recognition has gradually been carried out [ 12 ]. With the continuous development of computer technology, including software and hardware, and the arrival of the era of big data deep learning, it is possible to train and implement deeper and more complex convolutional neural networks.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Urban Street Public Space Art Design Indicators Based on Deep Convolutional Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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This paper analyzes and studies the structure and parameters of the VGGNet network model and selects the most commonly used and efficient VGG-16 as the prototype of the improved model. A multiscale sampling layer is added at the end of the VGG-16 convolution part so that the model can input images of any size for training and testing while reducing the number of neurons in the fully connected layer. This improves the training speed of the model under the premise of ensuring the accuracy. This paper uses multisource street spatial data combined with geographic information spatial analysis technology to measure and evaluate the spatial quality of streets in the main urban area. From the three dimensions of vitality, safety, and greenness of urban street space quality, a systematic structure for evaluation and analysis of street space quality is constructed. Street vitality includes eight index factors: entrance and exit density, street furniture density, street sketch density, street characteristic landscape density, POI density, POI diversity, commercial POI ratio, and street population density. There are five index factors: degree, roadside parking occupancy ratio, traffic signal system density, sidewalk width proportion, and street facility density. We use ArcGIS to build an index factor information database for statistical analysis and visualization. According to the natural discontinuous point classification method, the safety level of urban street public space is divided into five grades. The sample size of the first four grades has a small fluctuation range. The sample sizes are 153, 172, 153, and 158, respectively, accounting for 21%, 23%, 21%, and 21% of the total street samples, of which the first two grades occupy a total of 44%, so 44% of the streets in the main urban area have a low-quality level of street space. Level 5 has a sample of 102 streets, accounting for 14%, with an average street space quality value of 0.43.

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Using deep ensemble for influenza-like illness consultation rate prediction

Cited by 9 publications

References 26 publications

The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA

The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA

In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning

Prediction of Urban Street Public Space Art Design Indicators Based on Deep Convolutional Neural Network

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