Prediction of fall events during admission using eXtreme gradient boosting: a comparative validation study

Abstract As the performance of current fall risk assessment tools is limited, clinicians face significant challenges in identifying patients at risk of falling. This study proposes an automatic fall risk prediction model based on eXtreme gradient boosting (XGB), using a data-driven approach to the standardized medical records. This study analyzed a cohort of 639 participants (297 fall patients and 342 controls) from Chang Gung Memorial Hospital, Chiayi Branch, Taiwan. A derivation cohort of 507 participants (257 fall patients and 250 controls) was collected for constructing the prediction model using the XGB algorithm. A comparative validation of XGB and the Morse Fall Scale (MFS) was conducted with a prospective cohort of 132 participants (40 fall patients and 92 controls). The areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were used to compare the prediction models. This machine learning method provided a higher sensitivity than the standard method for fall risk stratification. In addition, the most important predictors found (Department of Neuro-Rehabilitation, Department of Surgery, cardiovascular medication use, admission from the Emergency Department, and bed rest) provided new information on in-hospital fall event prediction and the identification of patients with a high fall risk.

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Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm

IJIIS: International Journal of Informatics and Information Systems ◽

10.47738/ijiis.v3i2.65 ◽

2020 ◽

Vol 3 (2) ◽

pp. 54-59

Author(s):

Marco Febriadi Kokasih ◽

Adi Suryaputra Paramita

Keyword(s):

Prediction Model ◽

Prediction Models ◽

Gradient Boosting ◽

Fair Price ◽

Price Prediction ◽

Online Marketplace ◽

Extreme Gradient Boosting ◽

Property Owners ◽

Boosting Algorithm

Online marketplace in the field of property renting like Airbnb is growing. Many property owners have begun renting out their properties to fulfil this demand. Determining a fair price for both property owners and tourists is a challenge. Therefore, this study aims to create a software that can create a prediction model for property rent price. Variable that will be used for this study is listing feature, neighbourhood, review, date and host information. Prediction model is created based on the dataset given by the user and processed with Extreme Gradient Boosting algorithm which then will be stored in the system. The result of this study is expected to create prediction models for property rent price for property owners and tourists consideration when considering to rent a property. In conclusion, Extreme Gradient Boosting algorithm is able to create property rental price prediction with the average of RMSE of 10.86 or 13.30%.

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A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

Mathematics ◽

10.3390/math8091590 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1590

Author(s):

Muhammad Syafrudin ◽

Ganjar Alfian ◽

Norma Latif Fitriyani ◽

Muhammad Anshari ◽

Tony Hadibarata ◽

...

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Prediction Model ◽

Prediction Models ◽

Self Care ◽

Gradient Boosting ◽

Children With Disability ◽

Study Results ◽

Extreme Gradient Boosting ◽

Care Problems

Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs) with extreme gradient boosting (XGBoost) for predicting self-care problems of children with disability. Selecting the feature subset affects the model performance; thus, we utilize GA to optimize finding the optimum feature subsets toward improving the model’s performance. To validate the effectiveness of GA-XGBoost, we present six experiments: comparing GA-XGBoost with other machine learning models and previous study results, a statistical significant test, impact analysis of feature selection and comparison with other feature selection methods, and sensitivity analysis of GA parameters. During the experiments, we use accuracy, precision, recall, and f1-score to measure the performance of the prediction models. The results show that GA-XGBoost obtains better performance than other prediction models and the previous study results. In addition, we design and develop a web-based self-care prediction to help therapist diagnose the self-care problems of children with disabilities. Therefore, appropriate treatment/therapy could be performed for each child to improve their therapeutic outcome.

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Machine Learning-Based Cardiovascular Disease Prediction Model: A Cohort Study on the Korean National Health Insurance Service Health Screening Database

Diagnostics ◽

10.3390/diagnostics11060943 ◽

2021 ◽

Vol 11 (6) ◽

pp. 943

Author(s):

Joung Ouk (Ryan) Kim ◽

Yong-Suk Jeong ◽

Jin Ho Kim ◽

Jong-Weon Lee ◽

Dougho Park ◽

...

Keyword(s):

Machine Learning ◽

Health Insurance ◽

Prediction Model ◽

National Health Insurance ◽

National Health ◽

Prediction Models ◽

Characteristic Curve ◽

Health Screening ◽

Gradient Boosting ◽

Extreme Gradient Boosting

Background: This study proposes a cardiovascular diseases (CVD) prediction model using machine learning (ML) algorithms based on the National Health Insurance Service-Health Screening datasets. Methods: We extracted 4699 patients aged over 45 as the CVD group, diagnosed according to the international classification of diseases system (I20–I25). In addition, 4699 random subjects without CVD diagnosis were enrolled as a non-CVD group. Both groups were matched by age and gender. Various ML algorithms were applied to perform CVD prediction; then, the performances of all the prediction models were compared. Results: The extreme gradient boosting, gradient boosting, and random forest algorithms exhibited the best average prediction accuracy (area under receiver operating characteristic curve (AUROC): 0.812, 0.812, and 0.811, respectively) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the CVD prediction performance, compared to previously proposed prediction models. Preexisting CVD history was the most important factor contributing to the accuracy of the prediction model, followed by total cholesterol, low-density lipoprotein cholesterol, waist-height ratio, and body mass index. Conclusions: Our results indicate that the proposed health screening dataset-based CVD prediction model using ML algorithms is readily applicable, produces validated results and outperforms the previous CVD prediction models.

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Modelling treatment benefit for bexmarilimab (an anti-Clever-1 antibody and a novel macrophage checkpoint inhibitor) using phase I first-in-man trial data.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e14530 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e14530-e14530

Author(s):

Petri Bono ◽

Jussi Ekström ◽

Matti K Karvonen ◽

Jami Mandelin ◽

Jussi Koivunen

Keyword(s):

Feature Selection ◽

Prediction Model ◽

Phase I ◽

Missing Values ◽

Prediction Models ◽

Checkpoint Inhibitor ◽

Prediction Performance ◽

Gradient Boosting ◽

Treatment Benefit ◽

Extreme Gradient Boosting

e14530 Background: Bexmarilimab, an investigational immunotherapeutic antibody targeting Clever-1, is currently investigated in phase I/II MATINS study (NCT03733990) for advanced solid tumors. Machine learning (ML) based models combining extensive data could be generated to predict treatment responses to this first-in-class macrophage checkpoint inhibitor. Methods: 58 baseline features from 30 patients included in the part 1 of phase I/II MATINS trial were included in ML modelling. Seven patients were classified as benefitting from the therapy by RECIST 1.1 (PR or SD response in target or non-target lesions). Initial feature selection was done using a combination of domain knowledge and removal of features with several missing values resulting in 20 clinically relevant features from 25 patients. The remaining data was standardized and feature selection using variance analysis (ANOVA) based on F-values between response and features was performed. With this approach, the number of features could be further reduced as the prediction performance increased until the most important features were included in the model. Several prediction models were trained, and prediction performance evaluated using leave-one-out cross-validation (LOOCV), with and without SMOTE oversampling of the positive class of the training data inside each LOOCV fold. In LOOCV the prediction model was trained 25 times. Stacked meta classifier with SMOTE oversampling combining three classifiers: elastic-net logistic regression, random forest and extreme gradient boosting was chosen as the best performing prediction model. Results: Seven baseline features were associated with bexmarilimab treatment benefit. Increasing bexmarilimab dose and high tumor FoxP3 cells showed positive benefit. On contrary, high baseline blood neutrophils, CD4, T-cells, B-cells, and CXCL10 indicated negative relationship to the treatment benefit. The ML model trained with these seven features performed well in LOOCV as 6/7 benefitting and 16/18 non-benefitting were classified correctly, and all considered classification performance metrics were good. In feature importance analysis, low baseline CXCL10 and neutrophils were characterized as the most important predictors for treatment benefit with values of 0.19 and 0.16. Conclusions: This study highlights possibility of using ML models in predicting treatment benefit for novel cancer drugs such as bexmarilimab and boost the clinical development. These findings are in line of expected immune activation of bexmarilimab treatment. The generated ML models should be further validated in a larger patient cohort. Clinical trial information: NCT03733990.

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Evaluating Modeling and Validation Strategies for Tooth Loss

Journal of Dental Research ◽

10.1177/0022034519864889 ◽

2019 ◽

Vol 98 (10) ◽

pp. 1088-1095 ◽

Cited By ~ 2

Author(s):

J. Krois ◽

C. Graetz ◽

B. Holtfreter ◽

P. Brinkmann ◽

T. Kocher ◽

...

Keyword(s):

Tooth Loss ◽

Predictive Power ◽

Prediction Models ◽

Recursive Partitioning ◽

External Validation ◽

Model Development ◽

Gradient Boosting ◽

Extreme Gradient Boosting ◽

Complex Models ◽

Development And Validation

Prediction models learn patterns from available data (training) and are then validated on new data (testing). Prediction modeling is increasingly common in dental research. We aimed to evaluate how different model development and validation steps affect the predictive performance of tooth loss prediction models of patients with periodontitis. Two independent cohorts (627 patients, 11,651 teeth) were followed over a mean ± SD 18.2 ± 5.6 y (Kiel cohort) and 6.6 ± 2.9 y (Greifswald cohort). Tooth loss and 10 patient- and tooth-level predictors were recorded. The impact of different model development and validation steps was evaluated: 1) model complexity (logistic regression, recursive partitioning, random forest, extreme gradient boosting), 2) sample size (full data set or 10%, 25%, or 75% of cases dropped at random), 3) prediction periods (maximum 10, 15, or 20 y or uncensored), and 4) validation schemes (internal or external by centers/time). Tooth loss was generally a rare event (880 teeth were lost). All models showed limited sensitivity but high specificity. Patients’ age and tooth loss at baseline as well as probing pocket depths showed high variable importance. More complex models (random forest, extreme gradient boosting) had no consistent advantages over simpler ones (logistic regression, recursive partitioning). Internal validation (in sample) overestimated the predictive power (area under the curve up to 0.90), while external validation (out of sample) found lower areas under the curve (range 0.62 to 0.82). Reducing the sample size decreased the predictive power, particularly for more complex models. Censoring the prediction period had only limited impact. When the model was trained in one period and tested in another, model outcomes were similar to the base case, indicating temporal validation as a valid option. No model showed higher accuracy than the no-information rate. In conclusion, none of the developed models would be useful in a clinical setting, despite high accuracy. During modeling, rigorous development and external validation should be applied and reported accordingly.

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Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific

Atmosphere ◽

10.3390/atmos10060341 ◽

2019 ◽

Vol 10 (6) ◽

pp. 341 ◽

Cited By ~ 4

Author(s):

Qingwen Jin ◽

Xiangtao Fan ◽

Jian Liu ◽

Zhuxin Xue ◽

Hongdeng Jian

Keyword(s):

North Pacific ◽

Western North Pacific ◽

Prediction Models ◽

Weather Prediction ◽

Back Propagation ◽

Absolute Error ◽

Gradient Boosting ◽

Lead Times ◽

Intensity Prediction ◽

Extreme Gradient Boosting

Coastal cities in China are frequently hit by tropical cyclones (TCs), which result in tremendous loss of life and property. Even though the capability of numerical weather prediction models to forecast and track TCs has considerably improved in recent years, forecasting the intensity of a TC is still very difficult; thus, it is necessary to improve the accuracy of TC intensity prediction. To this end, we established a series of predictors using the Best Track TC dataset to predict the intensity of TCs in the Western North Pacific with an eXtreme Gradient BOOSTing (XGBOOST) model. The climatology and persistence factors, environmental factors, brainstorm features, intensity categories, and TC months are considered inputs for the models while the output is the TC intensity. The performance of the XGBOOST model was tested for very strong TCs such as Hato (2017), Rammasum (2014), Mujiage (2015), and Hagupit (2014). The results obtained show that the combination of inputs chosen were the optimal predictors for TC intensification with lead times of 6, 12, 18, and 24 h. Furthermore, the mean absolute error (MAE) of the XGBOOST model was much smaller than the MAEs of a back propagation neural network (BPNN) used to predict TC intensity. The MAEs of the forecasts with 6, 12, 18, and 24 h lead times for the test samples used were 1.61, 2.44, 3.10, and 3.70 m/s, respectively, for the XGBOOST model. The results indicate that the XGBOOST model developed in this study can be used to improve TC intensity forecast accuracy and can be considered a better alternative to conventional operational forecast models for TC intensity prediction.

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Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China

Clinical Infectious Diseases ◽

10.1093/cid/ciaa538 ◽

2020 ◽

Vol 71 (16) ◽

pp. 2079-2088 ◽

Cited By ~ 52

Author(s):

Kun Wang ◽

Peiyuan Zuo ◽

Yuwei Liu ◽

Meng Zhang ◽

Xiaofang Zhao ◽

...

Keyword(s):

Hospital Mortality ◽

Prediction Models ◽

Area Under The Curve ◽

Mortality Prediction ◽

Gradient Boosting ◽

Laboratory Model ◽

Training Cohort ◽

Clinical Model ◽

Extreme Gradient Boosting ◽

Mortality Prediction Models

Abstract Background This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19). Methods The training cohort included consecutive COVID-19 patients at the First People’s Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020. Results A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80–.95); threshold, −2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92–.99); threshold, −2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68–.93) and 0.88 (.75–.96) for the clinical model and laboratory model, respectively. Conclusions We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.

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Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Diagnostics ◽

10.3390/diagnostics11101909 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1909

Author(s):

Dougho Park ◽

Eunhwan Jeong ◽

Haejong Kim ◽

Hae Wook Pyun ◽

Haemin Kim ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Functional Outcome ◽

Outcome Prediction ◽

Prediction Models ◽

Gradient Boosting ◽

Support Vector ◽

Extreme Gradient Boosting

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

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An explainable XGBoost–based approach towards assessing the risk of cardiovascular disease in patients with Type 2 Diabetes Mellitus

10.36227/techrxiv.12942299.v1 ◽

2020 ◽

Author(s):

Maria Athanasiou ◽

Konstantina Sfrintzeri ◽

Konstantia Zarkogianni ◽

Anastasia Thanopoulou ◽

Konstantina S. Nikita

Keyword(s):

Diabetes Mellitus ◽

Cardiovascular Disease ◽

Risk Prediction ◽

Prediction Models ◽

Therapeutic Interventions ◽

Gradient Boosting ◽

Cvd Risk ◽

Medical Visits ◽

Extreme Gradient Boosting

<div> <div> <div> <p>Cardiovascular Disease (CVD) is an important cause of disability and death among individuals with Diabetes Mellitus (DM). International clinical guidelines for the management of Type 2 DM (T2DM) are founded on primary and secondary prevention and favor the evaluation of CVD related risk factors towards appropriate treatment initiation. CVD risk prediction models can provide valuable tools for optimizing the frequency of medical visits and performing timely preventive and therapeutic interventions against CVD events. The integration of explainability modalities in these models can enhance human understanding on the reasoning process, maximize transparency and embellish trust towards the models’ adoption in clinical practice. The aim of the present study is to develop and evaluate an explainable personalized risk prediction model for the fatal or non-fatal CVD incidence in T2DM individuals. An explainable approach based on the eXtreme Gradient Boosting (XGBoost) and the Tree SHAP (SHapley Additive exPlanations) method is deployed for the calculation of the 5-year CVD risk and the generation of individual explanations on the model’s decisions. Data from the 5- year follow up of 560 patients with T2DM are used for development and evaluation purposes. The obtained results (AUC=71.13%) indicate the potential of the proposed approach to handle the unbalanced nature of the used dataset, while providing clinically meaningful insights about the ensemble model’s decision process. </p> </div> </div> </div>

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Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.778306 ◽

2021 ◽

Vol 8 ◽

Author(s):

Ming-Hui Hung ◽

Ling-Chieh Shih ◽

Yu-Ching Wang ◽

Hsin-Bang Leu ◽

Po-Hsun Huang ◽

...

Keyword(s):

Machine Learning ◽

Clinical Characteristics ◽

Prediction Models ◽

External Validation ◽

Uncontrolled Hypertension ◽

Gradient Boosting ◽

Masked Hypertension ◽

Internal Validation ◽

Hypertensive Patients ◽

Extreme Gradient Boosting

Objective: This study aimed to develop machine learning-based prediction models to predict masked hypertension and masked uncontrolled hypertension using the clinical characteristics of patients at a single outpatient visit.Methods: Data were derived from two cohorts in Taiwan. The first cohort included 970 hypertensive patients recruited from six medical centers between 2004 and 2005, which were split into a training set (n = 679), a validation set (n = 146), and a test set (n = 145) for model development and internal validation. The second cohort included 416 hypertensive patients recruited from a single medical center between 2012 and 2020, which was used for external validation. We used 33 clinical characteristics as candidate variables to develop models based on logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGboost), and artificial neural network (ANN).Results: The four models featured high sensitivity and high negative predictive value (NPV) in internal validation (sensitivity = 0.914–1.000; NPV = 0.853–1.000) and external validation (sensitivity = 0.950–1.000; NPV = 0.875–1.000). The RF, XGboost, and ANN models showed much higher area under the receiver operating characteristic curve (AUC) (0.799–0.851 in internal validation, 0.672–0.837 in external validation) than the LR model. Among the models, the RF model, composed of 6 predictor variables, had the best overall performance in both internal and external validation (AUC = 0.851 and 0.837; sensitivity = 1.000 and 1.000; specificity = 0.609 and 0.580; NPV = 1.000 and 1.000; accuracy = 0.766 and 0.721, respectively).Conclusion: An effective machine learning-based predictive model that requires data from a single clinic visit may help to identify masked hypertension and masked uncontrolled hypertension.

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