scholarly journals Identification of Metabolic Syndrome Based on Anthropometric, Blood and Spirometric Risk Factors Using Machine Learning

2020 ◽  
Vol 10 (21) ◽  
pp. 7741
Author(s):  
Sang Yeob Kim ◽  
Gyeong Hee Nam ◽  
Byeong Mun Heo
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Strong Association ◽  
Forced Expiratory Volume ◽  
Binary Logistic Regression Analysis ◽  
Cross Sectional Survey ◽  
Cross Sectional

Metabolic syndrome (MS) is an aggregation of coexisting conditions that can indicate an individual’s high risk of major diseases, including cardiovascular disease, stroke, cancer, and type 2 diabetes. We conducted a cross-sectional survey to evaluate potential risk factor indicators by identifying relationships between MS and anthropometric and spirometric factors along with blood parameters among Korean adults. A total of 13,978 subjects were enrolled from the Korea National Health and Nutrition Examination Survey. Statistical analysis was performed using a complex sampling design to represent the entire Korean population. We conducted binary logistic regression analysis to evaluate and compare potential associations of all included factors. We constructed prediction models based on Naïve Bayes and logistic regression algorithms. The performance evaluation of the prediction model improved the accuracy with area under the curve (AUC) and calibration curve. Among all factors, triglyceride exhibited a strong association with MS in both men (odds ratio (OR) = 2.711, 95% confidence interval (CI) [2.328–3.158]) and women (OR = 3.515 [3.042–4.062]). Regarding anthropometric factors, the waist-to-height ratio demonstrated a strong association in men (OR = 1.511 [1.311–1.742]), whereas waist circumference was the strongest indicator in women (OR = 2.847 [2.447–3.313]). Forced expiratory volume in 6s and forced expiratory flow 25–75% strongly associated with MS in both men (OR = 0.822 [0.749–0.903]) and women (OR = 1.150 [1.060–1.246]). Wrapper-based logistic regression prediction model showed the highest predictive power in both men and women (AUC = 0.868 and 0.932, respectively). Our findings revealed that several factors were associated with MS and suggested the potential of employing machine learning models to support the diagnosis of MS.

scholarly journals A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population

2021 ◽  
Vol 9 ◽  
Author(s):  
Jie Liu ◽  
Jian Zhang ◽  
Haodong Huang ◽  
Yunting Wang ◽  
Zuyue Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Retrospective Study ◽  
Kawasaki Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Ivig Resistance ◽  
Independent Risk Factors

Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms.Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models.Results: In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067–1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270–1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008–1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996–1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575).Conclusion: The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.

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.

Interpretable Machine Learning–Based Prediction of Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Transsphenoidal Pituitary Surgery: A Pilot Study

2022 ◽  
Author(s):  
Pier Paolo Mattogno ◽  
Valerio M. Caccavella ◽  
Martina Giordano ◽  
Quintino G. D'Alessandris ◽  
Sabrina Chiloiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebrospinal Fluid ◽  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Cerebrospinal Fluid Leakage ◽  
Tumor Diameter ◽  
Predictive Values ◽  
Multivariable Logistic Regression ◽  
Csf Leakage

Abstract Purpose Transsphenoidal surgery (TSS) for pituitary adenomas can be complicated by the occurrence of intraoperative cerebrospinal fluid (CSF) leakage (IOL). IOL significantly affects the course of surgery predisposing to the development of postoperative CSF leakage, a major source of morbidity and mortality in the postoperative period. The authors trained and internally validated the Random Forest (RF) prediction model to preoperatively identify patients at high risk for IOL. A locally interpretable model-agnostic explanations (LIME) algorithm is employed to elucidate the main drivers behind each machine learning (ML) model prediction. Methods The data of 210 patients who underwent TSS were collected; first, risk factors for IOL were identified via conventional statistical methods (multivariable logistic regression). Then, the authors trained, optimized, and audited a RF prediction model. Results IOL reported in 45 patients (21.5%). The recursive feature selection algorithm identified the following variables as the most significant determinants of IOL: Knosp's grade, sellar Hardy's grade, suprasellar Hardy's grade, tumor diameter (on X, Y, and Z axes), intercarotid distance, and secreting status (nonfunctioning and growth hormone [GH] secreting). Leveraging the predictive values of these variables, the RF prediction model achieved an area under the curve (AUC) of 0.83 (95% confidence interval [CI]: 0.78; 0.86), significantly outperforming the multivariable logistic regression model (AUC = 0.63). Conclusion A RF model that reliably identifies patients at risk for IOL was successfully trained and internally validated. ML-based prediction models can predict events that were previously judged nearly unpredictable; their deployment in clinical practice may result in improved patient care and reduced postoperative morbidity and healthcare costs.

scholarly journals Comparison of Machine Learning With Logistic Regression for Prediction of Chronic Kidney Disease in the Thai Adult Population

2021 ◽  
Vol 44 (4) ◽  
pp. 1-12
Author(s):  
Ratchainant Thammasudjarit ◽  
Punnathorn Ingsathit ◽  
Sigit Ari Saputro ◽  
Atiporn Ingsathit ◽  
Ammarin Thakkinstian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Logistic Regression ◽  
Random Forest ◽  
Kidney Disease ◽  
Decision Tree ◽  
Prediction Model ◽  
Prediction Models ◽  
The Neural Network

Background: Chronic kidney disease (CKD) takes huge amounts of resources for treatments. Early detection of patients by risk prediction model should be useful in identifying risk patients and providing early treatments. Objective: To compare the performance of traditional logistic regression with machine learning (ML) in predicting the risk of CKD in Thai population. Methods: This study used Thai Screening and Early Evaluation of Kidney Disease (SEEK) data. Seventeen features were firstly considered in constructing prediction models using logistic regression and 4 MLs (Random Forest, Naïve Bayes, Decision Tree, and Neural Network). Data were split into train and test data with a ratio of 70:30. Performances of the model were assessed by estimating recall, C statistics, accuracy, F1, and precision. Results: Seven out of 17 features were included in the prediction models. A logistic regression model could well discriminate CKD from non-CKD patients with the C statistics of 0.79 and 0.78 in the train and test data. The Neural Network performed best among ML followed by a Random Forest, Naïve Bayes, and a Decision Tree with the corresponding C statistics of 0.82, 0.80, 0.78, and 0.77 in training data set. Performance of these corresponding models in testing data decreased about 5%, 3%, 1%, and 2% relative to the logistic model by 2%. Conclusions: Risk prediction model of CKD constructed by the logit equation may yield better discrimination and lower tendency to get overfitting relative to ML models including the Neural Network and Random Forest.  

scholarly journals Development of prediction models of spontaneous ureteral stone passage through machine learning: Comparison with conventional statistical analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260517
Author(s):  
Jee Soo Park ◽  
Dong Wook Kim ◽  
Dongu Lee ◽  
Taeju Lee ◽  
Kyo Chul Koo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Treatment Guidelines ◽  
Ureteral Stone ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Ureteral Stones ◽  
Stone Passage

Objectives To develop a prediction model of spontaneous ureteral stone passage (SSP) using machine learning and logistic regression and compare the performance of the two models. Indications for management of ureteral stones are unclear, and the clinician determines whether to wait for SSP or perform active treatment, especially in well-controlled patients, to avoid unwanted complications. Therefore, suggesting the possibility of SSP would help make a clinical decision regarding ureteral stones. Methods Patients diagnosed with unilateral ureteral stones at our emergency department between August 2014 and September 2018 were included and underwent non-contrast-enhanced computed tomography 4 weeks from the first stone episode. Predictors of SSP were applied to build and validate the prediction model using multilayer perceptron (MLP) with the Keras framework. Results Of 833 patients, SSP was observed in 606 (72.7%). SSP rates were 68.2% and 75.6% for stone sizes 5–10 mm and <5 mm, respectively. Stone opacity, location, and whether it was the first ureteral stone episode were significant predictors of SSP. Areas under the curve (AUCs) for receiver operating characteristic (ROC) curves for MLP, and logistic regression were 0.859 and 0.847, respectively, for stones <5 mm, and 0.881 and 0.817, respectively, for 5–10 mm stones. Conclusion SSP prediction models were developed in patients with well-controlled unilateral ureteral stones; the performance of the models was good, especially in identifying SSP for 5–10-mm ureteral stones without definite treatment guidelines. To further improve the performance of these models, future studies should focus on using machine learning techniques in image analysis.

scholarly journals Knowledge and prevalence of energy drinks consumption in Shanghai, China: a cross-sectional survey of adolescents

2021 ◽  
Vol 34 (3) ◽  
pp. e100389
Author(s):  
Ruyan Luo ◽  
Rao Fu ◽  
Lu Dong ◽  
Zheyi Du ◽  
Wei Sun ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Adverse Effects ◽  
Life Events ◽  
Negative Attitude ◽  
Binary Logistic Regression ◽  
Energy Drinks ◽  
Binary Logistic Regression Analysis ◽  
Cross Sectional Survey ◽  
Related Factors ◽  
Cross Sectional

BackgroundIn recent years, energy drinks (EDs) have been widely used among young people around the world. The extensive use of EDs also affects the sleep and exercise of adolescents.AimsThis study aimed to investigate the consumption of EDs, the knowledge, attitude towards EDs and associated factors of EDs consumption among adolescents in Shanghai, China.MethodsA total of 4608 adolescents completed a self-administered questionnaire assessing EDs use history, knowledge and attitude towards EDs. Adolescent Self-rating Life Events Checklist (ASLEC) was used to assess their life events. All participants were divided into two groups based on whether they used them or not. t-test and χ2 test were used to compare the differences between the two groups, and binary logistic regression analysis was used to investigate the related factors for EDs consumption.Results70.5% of the participants reported having ever used EDs. The main avenues to getting information on EDs were from advertisements. 67.56% of them believed that EDs had adverse effects on health. 22.09% of the participants and 31.55% of their parents took a negative attitude towards EDs. Compared with the non-consumption group, participants in the consumption group were likely to be male, with older age, identified EDs more correctly and did not believe EDs had adverse effects, with more positive attitude and higher ASLEC score. Logistic regression results showed that gender, age, attitude of parents and themselves, knowledge of EDs and ASLEC score significantly predicted EDs consumption.ConclusionEDs consumption was popular among adolescents in Shanghai, and the tailored intervention programmes need to be developed based on the characteristics of adolescents.

scholarly journals A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Haile Mekonnen Fenta ◽  
Temesgen Zewotir ◽  
Essey Kebede Muluneh
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Child Death ◽  
Area Under The Curve ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Machine Learning Classification ◽  
Under Five ◽  
Child Undernutrition ◽  
Sensitivity Specificity

Abstract Background Undernutrition is the main cause of child death in developing countries. This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and to identify the most important predictors. Method The study employed ML techniques using retrospective cross-sectional survey data from Ethiopia, a national-representative data collected in the year (2000, 2005, 2011, and 2016). We explored six commonly used ML algorithms; Logistic regression, Least Absolute Shrinkage and Selection Operator (L-1 regularization logistic regression), L-2 regularization (Ridge), Elastic net, neural network, and random forest (RF). Sensitivity, specificity, accuracy, and area under the curve were used to evaluate the performance of those models. Results Based on different performance evaluations, the RF algorithm was selected as the best ML model. In the order of importance; urban–rural settlement, literacy rate of parents, and place of residence were the major determinants of disparities of nutritional status for under-five children among Ethiopian administrative zones. Conclusion Our results showed that the considered machine learning classification algorithms can effectively predict the under-five undernutrition status in Ethiopian administrative zones. Persistent under-five undernutrition status was found in the northern part of Ethiopia. The identification of such high-risk zones could provide useful information to decision-makers trying to reduce child undernutrition.

scholarly journals Metabolic Syndrome and Pulmonary Function Indices

2018 ◽  
Vol 25 (3) ◽  
pp. 261-269
Author(s):  
Gholamreza Pouryaghoub ◽  
Ramin Mehrdad ◽  
Mohammad Mehraban
Keyword(s):  
Metabolic Syndrome ◽  
Lung Function ◽  
Density Lipoprotein ◽  
Forced Expiratory Volume ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Adult Men ◽  
Total Prevalence ◽  
Independent Association ◽  
Restrictive Pattern

Abstract Background and aims: Metabolic syndrome (MetS) is a collection of metabolic risk factors including increased waist circumference (WC), elevated blood pressure (BP), increased triglyceride (TG), decreased high density lipoprotein (HDL-C) and increased fasting blood sugar (FBS). We aimed to examine the relevance between the MetS and its components with reduced lung functions in adult men. Material and method: A total of 3899 adult men underwent screening examination between 2015-2016 in a cross-sectional survey. Results: The mean (± SD) age of our population was 37.25 (± 4.9) years. The overall prevalence of MetS was 7.6%. The total prevalence of reduced lung function in men with MetS was 13.8%. The most common type of reduced lung function was the restrictive pattern (7.1%). The forced expiratory volume of first second (FEV1) and forced vital capacity (FVC) values were significantly lower in men with MetS (both p<0.001). Also these values were significantly lower in diabetic men compared to non-diabetics and those with impaired fasting glucose (IFG). WC and HDL were the most potent predictors of reduced FEV1 and FVC. Conclusions: We obtained a positive independent association between MetS and reduced lung function in adult men which may be related mainly due to increased WC and decreased HDL.

scholarly journals Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry

2018 ◽  
Vol 15 (11) ◽  
pp. 2571 ◽  
Author(s):  
Byeong Mun Heo ◽  
Keun Ho Ryu
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Statistical Significance ◽  
Characteristic Curve ◽  
Binary Logistic Regression ◽  
Blood Parameters ◽  
Binary Logistic Regression Analysis

Hypertension and prehypertension are risk factors for cardiovascular diseases. However, the associations of both prehypertension and hypertension with anthropometry, blood parameters, and spirometry have not been investigated. The purpose of this study was to identify the risk factors for prehypertension and hypertension in middle-aged Korean adults and to study prediction models of prehypertension and hypertension combined with anthropometry, blood parameters, and spirometry. Binary logistic regression analysis was performed to assess the statistical significance of prehypertension and hypertension, and prediction models were developed using logistic regression, naïve Bayes, and decision trees. Among all risk factors for prehypertension, body mass index (BMI) was identified as the best indicator in both men [odds ratio (OR) = 1.429, 95% confidence interval (CI) = 1.304–1.462)] and women (OR = 1.428, 95% CI = 1.204–1.453). In contrast, among all risk factors for hypertension, BMI (OR = 1.993, 95% CI = 1.818–2.186) was found to be the best indicator in men, whereas the waist-to-height ratio (OR = 2.071, 95% CI = 1.884–2.276) was the best indicator in women. In the prehypertension prediction model, men exhibited an area under the receiver operating characteristic curve (AUC) of 0.635, and women exhibited a predictive power with an AUC of 0.777. In the hypertension prediction model, men exhibited an AUC of 0.700, and women exhibited an AUC of 0.845. This study proposes various risk factors for prehypertension and hypertension, and our findings can be used as a large-scale screening tool for controlling and managing hypertension.

Knowledge and Practice of Mothers Regarding the Prevention and Management of Malnutrition Among Preschool Children – A Cross Sectional Survey

2017 ◽  
Vol 9 (5) ◽  
Author(s):  
Mahfouz R. Nath ◽  
C. Kanniammal
Keyword(s):  
Preschool Children ◽  
Good Practice ◽  
Strong Association ◽  
Educational Status ◽  
Nursing Interventions ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Economic Class ◽  
Common Causes ◽  
Knowledge And Practice

Malnutrition remains one of the most common causes of morbidity and mortality among children throughout the world. Malnutrition has been responsible, directly or indirectly for 60% of the 10.9 million deaths annually among children under five. The research study was aimed to assess the knowledge and practice of mothers of preschool children regarding the prevention and management of malnutrition. The design used was descriptive cross sectional survey. The study was conducted in a coastal setting of Trivandrum district with a sample size of 115. Data collection was done by self administered structured questionnaire by conducting mothers meeting at selected Anganwadis. According to the results of the study 19.1% of mothers had good knowledge and 34.8 % of mothers had poor knowledge. Regarding practice only 24.3 % of mothers reported good practice while 36.6 % of mothers reported poor practice. There was a strong association between the knowledge and Practice of mothers and selected socio demographic variables such as educational status of mothers and socio economic class (p less than 0.01)). The study findings can be used for planning targeted nursing interventions in coastal areas for mothers of preschool children.

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