Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study (Preprint)

BACKGROUND The recent global outbreak of coronavirus disease (COVID-19) is affecting many countries worldwide. Iran is one of the top 10 most affected countries. Search engines provide useful data from populations, and these data might be useful to analyze epidemics. Utilizing data mining methods on electronic resources’ data might provide a better insight into the COVID-19 outbreak to manage the health crisis in each country and worldwide. OBJECTIVE This study aimed to predict the incidence of COVID-19 in Iran. METHODS Data were obtained from the Google Trends website. Linear regression and long short-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases. All models were evaluated using 10-fold cross-validation, and root mean square error (RMSE) was used as the performance metric. RESULTS The linear regression model predicted the incidence with an RMSE of 7.562 (SD 6.492). The most effective factors besides previous day incidence included the search frequency of handwashing, hand sanitizer, and antiseptic topics. The RMSE of the LSTM model was 27.187 (SD 20.705). CONCLUSIONS Data mining algorithms can be employed to predict trends of outbreaks. This prediction might support policymakers and health care managers to plan and allocate health care resources accordingly.

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Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study

JMIR Public Health and Surveillance ◽

10.2196/18828 ◽

2020 ◽

Vol 6 (2) ◽

pp. e18828 ◽

Cited By ~ 51

Author(s):

Seyed Mohammad Ayyoubzadeh ◽

Seyed Mehdi Ayyoubzadeh ◽

Hoda Zahedi ◽

Mahnaz Ahmadi ◽

Sharareh R Niakan Kalhori

Keyword(s):

Data Mining ◽

Health Care ◽

Linear Regression ◽

Short Term Memory ◽

Google Trends ◽

Health Care Managers ◽

Health Crisis ◽

Data Mining Algorithms ◽

Performance Metric ◽

Mining Algorithms

Background The recent global outbreak of coronavirus disease (COVID-19) is affecting many countries worldwide. Iran is one of the top 10 most affected countries. Search engines provide useful data from populations, and these data might be useful to analyze epidemics. Utilizing data mining methods on electronic resources’ data might provide a better insight into the COVID-19 outbreak to manage the health crisis in each country and worldwide. Objective This study aimed to predict the incidence of COVID-19 in Iran. Methods Data were obtained from the Google Trends website. Linear regression and long short-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases. All models were evaluated using 10-fold cross-validation, and root mean square error (RMSE) was used as the performance metric. Results The linear regression model predicted the incidence with an RMSE of 7.562 (SD 6.492). The most effective factors besides previous day incidence included the search frequency of handwashing, hand sanitizer, and antiseptic topics. The RMSE of the LSTM model was 27.187 (SD 20.705). Conclusions Data mining algorithms can be employed to predict trends of outbreaks. This prediction might support policymakers and health care managers to plan and allocate health care resources accordingly.

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Comparison of the Predictive Capabilities of Several Data Mining Algorithms and Multiple Linear Regression in the Prediction of Body Weight by Means of Body Measurements in the Indigenous Beetal Goat of Pakistan

Pakistan Journal of Zoology ◽

10.17582/journal.pjz/2017.49.1.257.265 ◽

2017 ◽

Vol 49 (1) ◽

pp. 257-265 ◽

Cited By ~ 7

Author(s):

Ecevit Eyduran ◽

Daniel Zaborski ◽

Abdul Waheed ◽

Senol Celik ◽

Koksal Karadas ◽

...

Keyword(s):

Data Mining ◽

Body Weight ◽

Linear Regression ◽

Multiple Linear Regression ◽

Body Measurements ◽

Data Mining Algorithms ◽

Mining Algorithms ◽

Beetal Goat

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Comparison of data mining algorithms for pressure prediction of crude oil pipeline to identify congeal

E3S Web of Conferences ◽

10.1051/e3sconf/202132502002 ◽

2021 ◽

Vol 325 ◽

pp. 02002

Author(s):

Agus Santoso ◽

F. Danang Wijaya ◽

Noor Akhmad Setiawan ◽

Joko Waluyo

Keyword(s):

Data Mining ◽

Linear Regression ◽

Crude Oil ◽

Petroleum Industry ◽

Pipeline System ◽

Data Mining Algorithm ◽

Oil Pipeline ◽

Data Mining Algorithms ◽

Mining Algorithm ◽

Mining Algorithms

Data mining is applied in many areas. In oil and gas industries, data mining may be implemented to support the decision making in their operation to prevent a massive loss. One of serious problems in the petroleum industry is congeal phenomenon, since it leads to block crude oil flow during transport in a pipeline system. In the crude oil pipeline system, pressure online monitoring in the pipeline is usually implemented to control the congeal phenomenon. However, this system is not able to predict the pipeline pressure on the next several days. This research is purposed to compare the pressure prediction of the crude oil pipeline using data mining algorithms based on the real historical data from the petroleum field. To find the best algorithms, it was compared 4 data mining algorithms, i.e. Random Forest, Multilayer Perceptron (MLP), Decision Tree, and Linear Regression. As a result, the Linear Regression shows the best performance among the 4 algorithms with R2 = 0.55 and RMSE = 28.34. This research confirmed that data mining algorithm is a good method to be implemented in petroleum industry to predict the pressure of the crude oil pipeline, even the accuracy of the prediction values should be improved. To have better accuracy, it is necessary to collect more data and find better performance of the data mining algorithm

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An In-Depth Analysis of The Data Mining Algorithms and Their Effective Applicability in Health and Biomedical Informatics

International Journal of Research in Medical Sciences & Technology ◽

10.37648/ijrmst.v11i01.005 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Deeya Tangri

Keyword(s):

Data Mining ◽

Health Care ◽

Medical Science ◽

Massive Data ◽

Medical Field ◽

Data Mining Algorithms ◽

Data Mining Approach ◽

Depth Analysis ◽

Data Extract ◽

Mining Algorithms

Nowadays, the Health care industry is one of the fastest-growing industries. As we already know, health care has researched very widely, introducing many medical data that is not easy to mine. Data mining is an approach that helps to discover essential data from massive data or collection of data. So, in medical Science, there is a need for tools that help analyses the data, extract the significant result from massive data, and discover efficient use of information. Generally, three things are mandatory in medical for every patient. First is patient details, diagnosis and medications. Converting these data into a basic pattern for predicting the patient disease helps in early diagnosis. This research mainly focuses on the data mining approach, which is widely considered in the medical field.

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Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

Current Drug Safety ◽

10.2174/1574886313666181026100000 ◽

2019 ◽

Vol 14 (1) ◽

pp. 21-26 ◽

Cited By ~ 2

Author(s):

Viswam Subeesh ◽

Eswaran Maheswari ◽

Hemendra Singh ◽

Thomas Elsa Beulah ◽

Ann Mary Swaroop

Keyword(s):

Data Mining ◽

Adverse Event ◽

Adverse Events ◽

Reporting System ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Disproportionality Analysis ◽

Positive Signal ◽

Data Mining Algorithms ◽

Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

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