Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study

Fang, Zheng-Gang; Yang, Sheng; Lv, Cai-Xia; An, Shi; Wu, Wei

doi:10.1136/bmjopen-2021-056685

Cited by 47 publications

(32 citation statements)

References 36 publications

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“…( 2022 ) UK Build a short-term forecasting ML model for the Coronavirus pandemic GQ1, GQ2, SQ3 Fang et al. ( 2022 ) China Propose a data-driven XGBoost model for the prediction of COVID-19 GQ1, GQ2, SQ1, SQ3 Garetto et al. ( 2021 ) Italy Compared to traditional compartmental models, a time-modulated Hawkes process to model the spread of COVID-19.…”

Section: Resultsmentioning

confidence: 99%

Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics

Palermo

Policarpo

Costa

et al. 2022

Netw Model Anal Health Inform Bioinforma

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This systematic review aims to study and classify machine learning models that predict pandemics’ evolution within affected regions or countries. The advantage of this systematic review is that it allows the health authorities to decide what prediction model fits best depending upon the region’s criticality and optimize hospitals’ approaches to preparing and anticipating patient care. We searched ACM Digital Library, Biomed Central, BioRxiv+MedRxiv, BMJ, Computers and Applied Sciences, IEEEXplore, JMIR Medical Informatics, Medline Daily Updates, Nature, Oxford Academic, PubMed, Sage Online, ScienceDirect, Scopus, SpringerLink, Web of Science, and Wiley Online Library between 1 January 2020 and 31 July 2022. We divided the interventions into similarities between cumulative COVID-19 real cases and machine learning prediction models’ ability to track pandemics trending. We included 45 studies that rated low to high risk of bias. The standardized mean differences (SMD) for the two groups were 0.18, 95% CI, with interval of [0.01, 0.35], =0, and p value=0.04. We built a taxonomic analysis of the included studies and determined two domains: pandemics trending prediction models and geolocation tracking models. We performed the meta-analysis and data synthesis and got low publication bias because of missing results. The level of certainty varied from very low to high. By submitting the 45 studies on the risk of bias, the levels of certainty, the summary of findings, and the statistical analysis via the forest and funnel plots assessments, we could determine the satisfactory statistical significance homogeneity across the included studies to simulate the progress of the pandemics and help the healthcare authorities to take preventive decisions.

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Section: Resultsmentioning

confidence: 99%

Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics

Palermo

Policarpo

Costa

et al. 2022

Netw Model Anal Health Inform Bioinforma

View full text Add to dashboard Cite

show abstract

“…It is a decision tree-based ensemble machine learning approach that is frequently employed in data science. After utilizing an internal approach that aggregates the outcomes from several individual trees, precise forecasts can be obtained [ 29 ]. XGBoost was first introduced by Chen Tianqi and Carlos in 2011, and since then several researchers have refined and enhanced it for the follow-up study [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

“…As an example, Aler et al utilize XGBoost in the field of direct-diffuse solar radiation separation by creating two models [ 28 ]. Moreover, in infectious disease prediction such as COVID-19, the XGBoost achieved greater prediction accuracy [ 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh

2023

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In this study, we attempt to anticipate annual rice production in Bangladesh (1961–2020) using both the Autoregressive Integrated Moving Average (ARIMA) and the eXtreme Gradient Boosting (XGBoost) methods and compare their respective performances. On the basis of the lowest Corrected Akaike Information Criteria (AICc) values, a significant ARIMA (0, 1, 1) model with drift was chosen based on the findings. The drift parameter value shows that the production of rice positively trends upward. Thus, the ARIMA (0, 1, 1) model with drift was found to be significant. On the other hand, the XGBoost model for time series data was developed by changing the tunning parameters frequently with the greatest result. The four prominent error measures, such as mean absolute error (MAE), mean percentage error (MPE), root mean square error (RMSE), and mean absolute percentage error (MAPE), were used to assess the predictive performance of each model. We found that the error measures of the XGBoost model in the test set were comparatively lower than those of the ARIMA model. Comparatively, the MAPE value of the test set of the XGBoost model (5.38%) was lower than that of the ARIMA model (7.23%), indicating that XGBoost performs better than ARIMA at predicting the annual rice production in Bangladesh. Hence, the XGBoost model performs better than the ARIMA model in predicting the annual rice production in Bangladesh. Therefore, based on the better performance, the study forecasted the annual rice production for the next 10 years using the XGBoost model. According to our predictions, the annual rice production in Bangladesh will vary from 57,850,318 tons in 2021 to 82,256,944 tons in 2030. The forecast indicated that the amount of rice produced annually in Bangladesh will increase in the years to come.

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“…XGBoost is an advanced powerful ML model, which combines multiple decision trees to construct a strong model. XGboost has been used in health data analysis tasks and achieved remarkable performances (Khera et al, 2021 ; Pieszko and Slomka, 2021 ; Fang et al, 2022 ) As this is a multi-class classification task (having three classes, i.e., healthy control, mild PD, and severe PD to predict), a one-vs.-all strategy was used. Specifically, the predictive model was the composition of three base models, one predicting HCs from all PDs, one predicting mild PDs from HCs and severe PDs, and one predicting severe PDs from HCs and mild PDs.…”

Section: Methodsmentioning

confidence: 99%

Developing and testing a prediction model for periodontal disease using machine learning and big electronic dental record data

Patel

Téllez

et al. 2022

Front. Artif. Intell.

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Despite advances in periodontal disease (PD) research and periodontal treatments, 42% of the US population suffer from periodontitis. PD can be prevented if high-risk patients are identified early to provide preventive care. Prediction models can help assess risk for PD before initiation and progression; nevertheless, utilization of existing PD prediction models is seldom because of their suboptimal performance. This study aims to develop and test the PD prediction model using machine learning (ML) and electronic dental record (EDR) data that could provide large sample sizes and up-to-date information. A cohort of 27,138 dental patients and grouped PD diagnoses into: healthy control, mild PD, and severe PD was generated. The ML model (XGBoost) was trained (80% training data) and tested (20% testing data) with a total of 74 features extracted from the EDR. We used a five-fold cross-validation strategy to identify the optimal hyperparameters of the model for this one-vs.-all multi-class classification task. Our prediction model differentiated healthy patients vs. mild PD cases and mild PD vs. severe PD cases with an average area under the curve of 0.72. New associations and features compared to existing models were identified that include patient-level factors such as patient anxiety, chewing problems, speaking trouble, teeth grinding, alcohol consumption, injury to teeth, presence of removable partial dentures, self-image, recreational drugs (Heroin and Marijuana), medications affecting periodontium, and medical conditions such as osteoporosis, cancer, neurological conditions, infectious diseases, endocrine conditions, cardiovascular diseases, and gastroenterology conditions. This pilot study demonstrated promising results in predicting the risk of PD using ML and EDR data. The model may provide new information to the clinicians about the PD risks and the factors responsible for the disease progression to take preventive approaches. Further studies are warned to evaluate the prediction model's performance on the external dataset and determine its usability in clinical settings.

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Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study

Cited by 47 publications

References 36 publications

Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics

Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics

A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh

Developing and testing a prediction model for periodontal disease using machine learning and big electronic dental record data

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