Preventive Healthcare: A Neural Network Analysis of Behavioral Habits and Chronic Diseases.
The research aims to explore the association between behavioral habits and chronic diseases, and to identify a portfolio of risk factors for preventive healthcare. The data is taken from the Behavioral Risk Factor Surveillance System (BRFSS) database of the Centers for Disease Control and Prevention, for the year 2012. Using SPSS Modeler, we deploy neural networks to identify strong positive and negative associations between certain chronic diseases and behavioral habits. The data for 475,687 records from BRFS database included behavioral habit variables of consumption of soda and fruits/vegetables, alcohol, smoking, weekly working hours, and exercise; chronic disease variables of heart attack, stroke, asthma, and diabetes; and demographic variables of marital status, income, and age. Our findings indicate that with chronic conditions, behavioral habits of physical activity and fruit and vegetable consumption are negatively associated; soda, alcohol, and smoking are positively associated; and income and age are positively associated. We contribute to individual and national preventive healthcare by offering a portfolio of significant behavioral risk factors that enable individuals to make lifestyle changes and governments to frame campaigns and policies countering chronic conditions and promoting public health.
Koppelman School of Business, Brooklyn College of the City University of New York, Brooklyn, NY 11210, USA. VRaghupathi@brooklyn.cuny.edu.
Viju
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Raghupathi
Gabelli School of Business, Fordham University, New York, NY 10058, USA. Raghupathi@fordham.edu.
Wullianallur
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Raghupathi
SPSS modeler; association; bayesian network; behavioral habit; chronic disease; health care; neural network; preventive