Comput. Biol. Med. - Prediction of metabolic syndrome using artificial neural network system based on clinical data including insulin resistance index and serum adiponectin.

Tópicos

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Resumo

JECTIVE: This study aimed to predict the 6-year incidence of metabolic syndrome (MetS) using an artificial neural network (ANN) system and multiple logistic regression (MLR) analysis based on clinical factors, including the insulin resistance index calculated by homeostasis model assessment (HOMA-IR).DESIGN: Subjects were recruited from participants in annual health check-ups in both 2000 and 2006. A total of 410 Japanese male teachers and other workers at Keio University, 30-59 years of age at baseline, participated in this retrospective cohort study.MEASUREMENTS: Clinical parameters were randomly divided into a training dataset and a validation dataset, and the ANN system and MLR analysis were applied to predict individual incidences. The leave some out cross validation method was used for validation.RESULTS: The sensitivity of the prediction was 0.27 for the MLR model and 0.93 for the ANN system, while specificities were 0.95 and 0.91, respectively. Sensitivity analysis employing the ANN system identified BMI, age, diastolic blood pressure, HDL-cholesterol, LDL-cholesterol and HOMA-IR as important predictors, suggesting these factors to be non-linearly related to the outcome.CONCLUSION: We successfully predicted the 6-year incidence of MetS using an ANN system based on clinical data, including HOMA-IR and serum adiponectin, in Japanese male subjects.

Resumo Limpo

jectiv studi aim predict year incid metabol syndrom met use artifici neural network ann system multipl logist regress mlr analysi base clinic factor includ insulin resist index calcul homeostasi model assess homairdesign subject recruit particip annual health checkup total japanes male teacher worker keio univers year age baselin particip retrospect cohort studymeasur clinic paramet random divid train dataset valid dataset ann system mlr analysi appli predict individu incid leav cross valid method use validationresult sensit predict mlr model ann system specif respect sensit analysi employ ann system identifi bmi age diastol blood pressur hdlcholesterol ldlcholesterol homair import predictor suggest factor nonlinear relat outcomeconclus success predict year incid met use ann system base clinic data includ homair serum adiponectin japanes male subject

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