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

Cardiovascular atherosclerotic diseases represent the significant cause of death worldwide during the past few decades. Obesity is recognized as an independent factor for the development of the cardiovascular diseases. There is a strong correlation between the central (abdominal) type of obesity and the cardiovascular and metabolic diseases. Among a variety of anthropometric measurements of the abdominal fat size, sagittal abdominal diameter (SAD) has been proposed as the valid measurement of the visceral fat mass and cardiometabolic risk level. This paper presents a solution based on artificial neural networks (ANN) for estimating SAD low-limits for the adverse 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 that includes 1341 persons.