Estimating SAD Low-Limits for the Adverse Metabolic Profile by Using Artificial Neural Networks
Estimating SAD Low-Limits for the Adverse Metabolic Profile by Using Artificial Neural Networks

Summary/Abstract: Cardiovascular atherosclerotic diseasesrepresent the significant cause of death worldwideduring the past few decades. Obesity is recognized asan independent factor for the development of thecardiovascular diseases. There is a strong correlationbetween the central (abdominal) type of obesity and thecardiovascular and metabolic diseases. Among avariety of anthropometric measurements of theabdominal fat size, sagittal abdominal diameter (SAD)has been proposed as the valid measurement of thevisceral fat mass and cardiometabolic risk level. Thispaper presents a solution based on artificial neuralnetworks (ANN) for estimating SAD low-limits for theadverse metabolic profile. ANN inputs are: gender, age,body mass index, systolic and diastolic blood pressures,HDL-, LDL- and total cholesterol, triglycerides,glycemia, fibrinogen and uric acid. ANN output is SAD.ANN training and testing are done by dataset thatincludes 1341 persons.

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