Abstract

Objective: The first cause of death worldwide is cardiovascular disease (CVD). CVD covers a wide array of disorders, including diseases of the cardiac muscle and of the vascular system supplying the heart, brain, and other vital organs. This research aims to the comprehensive impact that a series of lifestyle data from a population has on the main cardiovascular risk factors. Such predictions of the influence of lifestyle on cardiovascular risk factors could be useful.

Material and methods: A cross-sectional study was designed; the subjects who lived in Minodar, Iran were interviewed by trained nurses using a structural questionnaire. Data were processed from a sample of 393 subjects of both sexes aged 26-79 years old. The output data were: high/low cholesterolemia, HDL-C cholesterol, triglyceridemia. The input data were: sex, age, build, weight, marital status, Individual’s status in the family, physical activity, hours of sleep per day, smoking, tobacco type, BMI. Two predictor models including artificial neural network and linear regression were applied.

Results: Logistic regression (LR) as a conventional model obtained poor prediction performance measure values. However, LR distinguished that relationships exist between inputs and dichotomous output variables (sex and BMI in TG and sex, weight and tobacco type in HDL-C and sex in total cholesterol as more significant parameters). On the other hand, artificial neural network as a more powerful model showed high response accuracy in predicting CVD risk factors. Such pleasing results could be attributed to the non-linear nature of ANN in problem solving which provides the opportunity to predict independent variables to dependent ones non-linearly.

Conclusion: The results displayed that our ANN-based model approach is very hopeful and may play a useful role in developing a better method for assessing the influence of lifestyle on cardiovascular risk factors.

Keywords: Cardiovascular risk factors; Lifestyle; Neural network; Logistic regression