scholarly journals Application of Data Mining Technology in Risk Prediction of Metabolic Syndrome in Oil Workers

2020 ◽  
Author(s):  
Jie Wang ◽  
Chao Li ◽  
Jing Li ◽  
Sheng Qin ◽  
Chunlei Liu ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Random Forest ◽  
Risk Prediction ◽  
Roc Curve ◽  
Prediction Models ◽  
Prediction Performance ◽  
Random Forest Model ◽  
Forest Model ◽  
The Metabolic Syndrome ◽  
Oil Workers

Abstract Background. The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people's health.In this paper, three kinds of risk prediction models applicable to the metabolic syndrome of oil workers were established, and the optimal models were found through comparison. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome.Methods. A total of 1,468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models.Results. The results showed that the accuracy of the three models in the training set was 83.45%, 94.21% and 86.34%, the sensitivity was 78.47%, 94.62% and 81.30%, the F1 score was 0.79, 0.93 and 0.83, and the area under the ROC curve was 0.894, 0.987 and 0.935, respectively. In the test set, the accuracy was 76.72%, 80.66% and 78.69%, the sensitivity was 70.00%, 77.50% and 68.33%, the F1 score was 0.70, 0.76 and 0.71, and the area under the ROC curve was 0.797, 0.861 and 0.855, respectively.Conclusions. The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

scholarly journals Development and internal validation of risk prediction model of metabolic syndrome in oil workers

2020 ◽  
Author(s):  
Jie Wang ◽  
Chao Li ◽  
Jing Li ◽  
Sheng Qin ◽  
Chunlei Liu ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Random Forest ◽  
Risk Prediction ◽  
Roc Curve ◽  
Prediction Performance ◽  
Random Forest Model ◽  
Optimal Model ◽  
Forest Model ◽  
The Metabolic Syndrome ◽  
Oil Workers

Abstract Background.The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people's health. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome.Objective.To develop and internally verify three risk prediction models for the metabolic syndrome of petroleum workers, compare the prediction performance of the three models, and find the optimal model.Methods. Design existing circumstances research. A total of 1,468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models.Results.The results showed that the accuracy of the three models in the training set was 83.45%, 94.21% and 86.34%, the sensitivity was 78.47%, 94.62% and 81.30%, the F1 score was 0.79, 0.93 and 0.83, the area under the ROC curve was 0.894, 0.987 and 0.935, and the Integrated Calibration Index was 0.074, 0.071 and 0.078, respectively. In the test set, the accuracy was 76.72%, 80.66% and 78.69%, the sensitivity was 70.00%, 77.50% and 68.33%, the F1 score was 0.70, 0.76 and 0.71, the area under the ROC curve was 0.797, 0.861 and 0.855, and the Integrated Calibration Index was 0.064, 0.051 and 0.096, respectively.Conclusions.The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

scholarly journals Development and internal validation of risk prediction model of metabolic syndrome in oil workers

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jie Wang ◽  
Chao Li ◽  
Jing Li ◽  
Sheng Qin ◽  
Chunlei Liu ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Random Forest ◽  
Health Management ◽  
Disease Risk ◽  
Prediction Performance ◽  
Random Forest Model ◽  
Brier Score ◽  
Internal Validation ◽  
Forest Model ◽  
Oil Workers

Abstract Background The prevalence of metabolic syndrome continues to rise sharply worldwide, seriously threatening people’s health. The optimal model can be used to identify people at high risk of metabolic syndrome as early as possible, to predict their risk, and to persuade them to change their adverse lifestyle so as to slow down and reduce the incidence of metabolic syndrome. Methods Design existing circumstances research. A total of 1468 workers from an oil company who participated in occupational health physical examination from April 2017 to October 2018 were included in this study. We established the Logistic regression model, the random forest model and the convolutional neural network model, and compared the prediction performance of the models according to the F1 score, sensitivity, accuracy and other indicators of the three models. Results The results showed that the accuracy of the three models was 82.49,95.98 and 92.03%, the sensitivity was 87.94,95.52 and 90.59%, the specificity was 74.54, 96.65 and 94.14%, the F1 score was 0.86,0.97 and 0.93, and the area under ROC curve was 0.88,0.96 and 0.92, respectively. The Brier score of the three models was 0.15, 0.08 and 0.12, Observed-expected ratio was 0.83, 0.97 and 1.13, and the Integrated Calibration Index was 0.075,0.073 and 0.074, respectively, and explained how the random forest model was used for individual disease risk score. Conclusions The study showed that the prediction performance of random forest model is better than other models, and the model has higher application value, which can better predict the risk of metabolic syndrome in oil workers, and provide corresponding theoretical basis for the health management of oil workers.

scholarly journals A novel atrial fibrillation prediction model for Chinese subjects: a nationwide cohort investigation of 682 237 study participants with random forest model

EP Europace ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 1307-1312 ◽  
Author(s):  
Wei-Syun Hu ◽  
Meng-Hsuen Hsieh ◽  
Cheng-Li Lin
Keyword(s):  
Atrial Fibrillation ◽  
Random Forest ◽  
Prediction Model ◽  
Roc Curve ◽  
Model Performance ◽  
Weighted Average ◽  
Random Forest Model ◽  
Test Set ◽  
Forest Model ◽  
Data Points

Abstract Aims We aimed to construct a random forest model to predict atrial fibrillation (AF) in Chinese population. Methods and results This study was comprised of 682 237 subjects with or without AF. Each subject had 19 features that included the subjects’ age, gender, underlying diseases, CHA2DS2-VASc score, and follow-up period. The data were split into train and test sets at an approximate 9:1 ratio: 614 013 data points were placed into the train set and 68 224 data points were placed into the test set. In this study, weighted average F1, precision, and recall values were used to measure prediction model performance. The F1, precision, and recall values were calculated across the train set, the test set, and all data. The area under receiving operating characteristic (ROC) curve was also used to evaluate the performance of the prediction model. The prediction model achieved a k-fold cross-validation accuracy of 0.979 (k = 10). In the test set, the prediction model achieved an F1 value of 0.968, precision value of 0.958, and recall value of 0.979. The area under ROC curve of the model was 0.948 (95% confidence interval 0.947–0.949). This model was validated with a separate dataset. Conclusions This study showed a novel AF risk prediction scheme for Chinese individuals with random forest model methodology.

scholarly journals Development and Validation of an Insulin Resistance Predicting Model Using a Machine-Learning Approach in a Population-Based Cohort in Korea

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 212
Author(s):  
Sunmin Park ◽  
Chaeyeon Kim ◽  
Xuangao Wu
Keyword(s):  
Machine Learning ◽  
Insulin Resistance ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Random Forest ◽  
Roc Curve ◽  
Genome Wide Association Study ◽  
Prediction Models ◽  
Risk Scores ◽  
A Genome

Background: Insulin resistance is a common etiology of metabolic syndrome, but receiver operating characteristic (ROC) curve analysis shows a weak association in Koreans. Using a machine learning (ML) approach, we aimed to generate the best model for predicting insulin resistance in Korean adults aged > 40 of the Ansan/Ansung cohort using a machine learning (ML) approach. Methods: The demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8842 participants were included. The polygenetic risk scores (PRS) generated by a genome-wide association study were added to represent the genetic impact of insulin resistance. They were divided randomly into the training (n = 7037) and test (n = 1769) sets. Potentially important features were selected in the highest area under the curve (AUC) of the ROC curve from 99 features using seven different ML algorithms. The AUC target was ≥0.85 for the best prediction of insulin resistance with the lowest number of features. Results: The cutoff of insulin resistance defined with HOMA-IR was 2.31 using logistic regression before conducting ML. XGBoost and logistic regression algorithms generated the highest AUC (0.86) of the prediction models using 99 features, while the random forest algorithm generated a model with 0.82 AUC. These models showed high accuracy and k-fold values (>0.85). The prediction model containing 15 features had the highest AUC of the ROC curve in XGBoost and random forest algorithms. PRS was one of 15 features. The final prediction models for insulin resistance were generated with the same nine features in the XGBoost (AUC = 0.86), random forest (AUC = 0.84), and artificial neural network (AUC = 0.86) algorithms. The model included the fasting serum glucose, ALT, total bilirubin, HDL concentrations, waist circumference, body fat, pulse, season to enroll in the study, and gender. Conclusion: The liver function, regular pulse checking, and seasonal variation in addition to metabolic syndrome components should be considered to predict insulin resistance in Koreans aged over 40 years.

scholarly journals Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality

2021 ◽  
Vol 11 (12) ◽  
pp. 1271
Author(s):  
Jaehyeong Cho ◽  
Jimyung Park ◽  
Eugene Jeong ◽  
Jihye Shin ◽  
Sangjeong Ahn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Postoperative Mortality ◽  
Random Forest Model ◽  
Forest Model ◽  
Laboratory Values ◽  
Increased Risk

Background: Several prediction models have been proposed for preoperative risk stratification for mortality. However, few studies have investigated postoperative risk factors, which have a significant influence on survival after surgery. This study aimed to develop prediction models using routine immediate postoperative laboratory values for predicting postoperative mortality. Methods: Two tertiary hospital databases were used in this research: one for model development and another for external validation of the resulting models. The following algorithms were utilized for model development: LASSO logistic regression, random forest, deep neural network, and XGBoost. We built the models on the lab values from immediate postoperative blood tests and compared them with the SASA scoring system to demonstrate their efficacy. Results: There were 3817 patients who had immediate postoperative blood test values. All models trained on immediate postoperative lab values outperformed the SASA model. Furthermore, the developed random forest model had the best AUROC of 0.82 and AUPRC of 0.13, and the phosphorus level contributed the most to the random forest model. Conclusions: Machine learning models trained on routine immediate postoperative laboratory values outperformed previously published approaches in predicting 30-day postoperative mortality, indicating that they may be beneficial in identifying patients at increased risk of postoperative death.

P1511MODELO PREDICTIVO DE SUPERVIVENCIA DE INTELIGENCIA ARTIFICIAL (BOSQUE ALEATORIO) EN HEMODIALISIS. DATOS DEL REGISTRO ANDALUSINA DE ENFERMEDADES RENALES. SICATA

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Manuel Benítez Sánchez ◽  
Guillermo Martín ◽  
Luis Gil Sacaluga ◽  
Maria Jose Garcia Cortes ◽  
Sergio García Marcos ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Roc Curve ◽  
Explanatory Models ◽  
Multivariate Model ◽  
Random Forest Model ◽  
Predictor Variables ◽  
Multivariate Logistic Regression ◽  
Analytical Technique ◽  
Forest Model

Abstract Background and Aims Random Forest (RF) is an analytical technique of Artificial Intelligence (AI) that consists of an assembly of trees built by bootstrapping (resampling with replacement). In each node a subset of predictor variables is selected and for them the best cut point is determined. Each division of the tree is based on a random sample of the predictors. The trees are as long as possible. In the construction of each RF tree a part of the observations is not used (37% approx.). It is called an out-of-bag (OOB) sample and is used to obtain an honest estimate of the predictive capacity of the model. So it does not require validation. In each analysis, a few hundred Regression or classification trees are carried out, depending on whether the response variable is numerical or qualitative respectively. The result is an average of the repeated predictions of the model (Bagging). RF allows to calculate the importance of the predictor variables, which can be used later to be included in a multivariate regression model. Method We analyzed 14750 records between 2011 and 2014 contained in Information System of the Autonomous Transplant Coordination of Andalusia (SICATA) a system that includes clinical-epidemiological variables, about anemia, bone bone metabolism, adequacy of dialysis and vascular access. 1911 patients presented the event of interest (exitus). Three predictive and explanatory models of survival are developed: 1-RF. 2-.Multivariate Logistic Regression. 3- Multivariate Logistic Regression that includes the important variables of the previous RF model. We compare them in terms of accuracy (AUC of the ROC curve). Results AUC of the ROC curve of the multivariate model without prior RF was: 0.75 AUC of the ROC curve of the multivariate model with previous RF was: 0.81. AUC of the ROC curve of the Random Forest model: 0.98 Conclusion The Random Forest model has a 98% discrimination in the mortality of patients on Hemodialysis, far superior to the classic multivariate analyzes. The Multivariate Logistic Regression performed with the important RF variables improves the AUC of the previous model 0.81 vs. 0.75.

scholarly journals A zero altered Poisson random forest model for genomic-enabled prediction

2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Osval Antonio Montesinos-López ◽  
Abelardo Montesinos-López ◽  
Brandon A Mosqueda-Gonzalez ◽  
José Cricelio Montesinos-López ◽  
José Crossa ◽  
...  
Keyword(s):  
Random Forest ◽  
Prediction Performance ◽  
Random Forest Model ◽  
Statistical Machine Learning ◽  
Excess Zeros ◽  
Forest Model ◽  
Generalized Poisson ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Count Response

Abstract In genomic selection choosing the statistical machine learning model is of paramount importance. In this paper, we present an application of a zero altered random forest model with two versions (ZAP_RF and ZAPC_RF) to deal with excess zeros in count response variables. The proposed model was compared with the conventional random forest (RF) model and with the conventional Generalized Poisson Ridge regression (GPR) using two real datasets, and we found that, in terms of prediction performance, the proposed zero inflated random forest model outperformed the conventional RF and GPR models.

scholarly journals Variant pathogenic prediction models VSRFM and VSRFM-s, the importance of splicing and allele frequency

2018 ◽  
Author(s):  
JL Cabrera-Alarcon ◽  
J Garcia-Martinez
Keyword(s):  
Random Forest ◽  
Allele Frequency ◽  
Prediction Models ◽  
Specific Model ◽  
Random Forest Model ◽  
Independent Data ◽  
Data Set ◽  
New Model ◽  
Forest Model

ABSTRACTCurrently, there are available several tools to predict the effect of variants, with the aim of classify variants in neutral or pathogenic. In this study, we propose a new model trained over ensemble scores with two particularities, first we consider minor frequency allele from gnomAD and second, we split variants based on their splicing for training each specific model. Variants Stacked Random Forest Model (VSRFM) was constructed for variants not involved in splicing and Variants Stacked Random Forest Model for splicing (VSRFM-s) was trained for variants affected by splicing. Comparing these scores with their constituent scores used as features, our models showed the best outcomes. These results were confirmed using an independent data set from Clinvar database, with similar results.

scholarly journals Predicting the required pre-surgery blood volume in surgical patients based on machine learning

2019 ◽  
Author(s):  
Ruilin Li ◽  
Xinyin Han ◽  
Liping Sun ◽  
Yannan Feng ◽  
Xiaolin Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Blood Transfusion ◽  
Blood Volume ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Random Forest Model ◽  
Surgical Patients ◽  
Forest Model ◽  
Formidable Challenge

AbstractPrecisely predicting the required pre-surgery blood volume (PBV) in surgical patients is a formidable challenge in China. Inaccurate estimation is associate with excessive costs, postponed surgeries and adverse outcome after surgery due to in sufficient supply or inventory. This study aimed to predict required PBV based on machine learning techniques. 181,027 medical documents over 6 years were cleaned and finally obtained 92,057 blood transfusion records. The blood transfusion and surgery related factors of perioperative patients, surgeons experience volumes and the actual volumes of transfused RBCs were extracted. 6 machine learning algorithms were used to build prediction models. The surgery patients received allogenic RBCs or without transfusion, had total volume less than 10 units, or had the latest laboratory examinations of pre-surgery within 7 days were included, providing 118,823 data points. 39 predictive factors related to the RBCs transfusion were identified. Random forest model was selected to predict the required PBV of RBCs with 72.9% accuracy and strikingly improved the accuracy by 30.4% compared with surgeons experience, where 90% of data was used for training. We tested and demonstrated that both the data-driven models and the random forest model achieved higher accuracy than surgeons experience. Furthermore, we developed a computational tool, PTRBC, to precisely estimate the required PBV in surgical patients and we believe this tool will find more applications in assisting clinician decisions, not only confined to making accurate pre-surgery blood requirement predicting.

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