Easy and Low-Cost Identification of Metabolic Syndrome in Patients Treated With Second-Generation Antipsychotics

Lin, Chao Cheng; Bai, Ya Mei; Chen, Jen Yeu; Hwang, Tzung‐Jeng; Chen, Tzu Ting; Chiu, Hung Wen; Li, Yu-Chuan

doi:10.4088/jcp.08m04628yel

Cited by 38 publications

(34 citation statements)

References 44 publications

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“…This is a non-parametric pattern recognition method that can recognize hidden patterns between independent and dependent variables [13]. Although ANN has been used in oncology, diabetes, hypertension, and other diseases [14]–[21], none have developed and evaluated the feasibility of the ANN model for predicting the risk of dyslipidemia in rural adults. Thus, it is unclear whether the ANN model is reliable and effective to identify those at high risk of dyslipidemia.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of a Simple and Effective Prediction Approach for Identifying Those at High Risk of Dyslipidemia in Rural Adult Residents

Wang

et al. 2012

PLoS ONE

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BackgroundDyslipidemia is an extremely prevalent but preventable risk factor for cardiovascular disease. However, many dyslipidemia patients remain undetected in resource limited settings. The study was performed to develop and evaluate a simple and effective prediction approach without biochemical parameters to identify those at high risk of dyslipidemia in rural adult population.MethodsDemographic, dietary and lifestyle, and anthropometric data were collected by a cross-sectional survey from 8,914 participants living in rural areas aged 35–78 years. There were 6,686 participants randomly selected into a training group for constructing the artificial neural network (ANN) and logistic regression (LR) prediction models. The remaining 2,228 participants were assigned to a validation group for performance comparisons of ANN and LR models. The predictors of dyslipidemia risk were identified from the training group using multivariate logistic regression analysis. Predictive performance was evaluated by receiver operating characteristic (ROC) curve.ResultsSome risk factors were significantly associated with dyslipidemia, including age, gender, educational level, smoking, high-fat diet, vegetable and fruit intake, family history, physical activity, and central obesity. For the ANN model, the sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive values were 90.41%, 76.66%, 3.87, 0.13, 76.33%, and 90.58%, respectively, while LR model were only 57.37%, 70.91%, 1.97, 0.60, 62.09%, and 66.73%, respectively. The area under the ROC cure (AUC) value of the ANN model was 0.86±0.01, showing more accurate overall performance than traditional LR model (AUC = 0.68±0.01, P<0.001).ConclusionThe ANN model is a simple and effective prediction approach to identify those at high risk of dyslipidemia, and it can be used to screen undiagnosed dyslipidemia patients in rural adult population. Further work is planned to confirm these results by incorporating multi-center and longer follow-up data.

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Section: Introductionmentioning

confidence: 99%

Development and Evaluation of a Simple and Effective Prediction Approach for Identifying Those at High Risk of Dyslipidemia in Rural Adult Residents

Wang

et al. 2012

PLoS ONE

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“…The results of the current study are consistent with the findings of other studies that evaluated the efficiency of ANN on the prediction of other diseases, including hypertension (26), diabetes (27), and coronary artery disease (28), predicting metabolic syndrome (29), complications of diabetes (30), gastric cancer (31), and other cancerous lesions (25,32), and predicting mortality in patients with sepsis (33); in all the mentioned studies, ANN had a higher efficacy than logistic regression in predicting the studied outcomes.…”

Section: Discussionsupporting

confidence: 81%

Factors Affecting Endometriosis in Women of Reproductive Age: The Differences Between the Results of Neural Network and Logistic Regression

et al. 2018

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Background: Endometriosis is a common gynecologic problem in women of reproductive age around the globe. The aim of this study was to specify the factors influencing endometriosis in women of reproductive age using logistic regression and artificial neural network (ANN). Methods:The data of this case-control study was obtained from the medical records in Rasoul-e-Akram hospital, Tehran. Patients, who underwent laparoscopy from 2007 to 2015 and were diagnosed with endometriosis, were selected as the case group (n = 250), and patients diagnosed without endometriosis served as controls (n = 250). To investigate the factors affecting the occurrence of endometriosis, ANN and logistic regression were used and for evaluating the efficiency of the two methods, the area under the ROC curve (AUC) was used. To analyze the data, SPSS (version 22) and R (version 3.2.1) software were used.

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“…Theoretically this three-subtype model might serve as a framework to examine the different psychopathologic processes regarding the heterogeneity of schizophrenia. In the future we will use an artificial neural network to offer convenient clinical applications, 50 and use prospective studies on FE schizophrenia with stringent follow-up, as well as psychopharmacologic, neurobiologic, and genetic studies to retest this three-subtype model.…”

Section: Discussionmentioning

confidence: 99%

Medium-term course and outcome of schizophrenia depicted by the sixth-month subtype after an acute episode

Liu

Chen

Shiu

et al. 2012

Journal of the Formosan Medical Association

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Easy and Low-Cost Identification of Metabolic Syndrome in Patients Treated With Second-Generation Antipsychotics

Abstract: Our study developed accurate ANN and logistic regression models to detect MetS in patients with SGA treatment. The models are likely to provide a noninvasive tool for large-scale screening of MetS in this group of patients.

Cited by 38 publications

References 44 publications

Development and Evaluation of a Simple and Effective Prediction Approach for Identifying Those at High Risk of Dyslipidemia in Rural Adult Residents

Development and Evaluation of a Simple and Effective Prediction Approach for Identifying Those at High Risk of Dyslipidemia in Rural Adult Residents

Factors Affecting Endometriosis in Women of Reproductive Age: The Differences Between the Results of Neural Network and Logistic Regression

Medium-term course and outcome of schizophrenia depicted by the sixth-month subtype after an acute episode

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