Hussain A Isma'eel1, George E Sakr1, Mohamad M Almedawar1, Jihan Fathallah1, Torkom Garabedian1, Savo Bou Zein Eddine1, Lara Nasreddine1, Imad H Elhajj1. 1. 1 Division of Cardiology, Department of Internal Medicine, American University of Beirut, Beirut, Lebanon ; 2 Vascular Medicine Program, American University of Beirut Medical Center, Beirut, Lebanon ; 3 Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA ; 4 Department of Electrical & Computer Engineering, 5 Department of Nutrition & Food Sciences, American University of Beirut, Beirut, Lebanon.
Abstract
BACKGROUND: High dietary salt intake is directly linked to hypertension and cardiovascular diseases (CVDs). Predicting behaviors regarding salt intake habits is vital to guide interventions and increase their effectiveness. We aim to compare the accuracy of an artificial neural network (ANN) based tool that predicts behavior from key knowledge questions along with clinical data in a high cardiovascular risk cohort relative to the least square models (LSM) method. METHODS: We collected knowledge, attitude and behavior data on 115 patients. A behavior score was calculated to classify patients' behavior towards reducing salt intake. Accuracy comparison between ANN and regression analysis was calculated using the bootstrap technique with 200 iterations. RESULTS: Starting from a 69-item questionnaire, a reduced model was developed and included eight knowledge items found to result in the highest accuracy of 62% CI (58-67%). The best prediction accuracy in the full and reduced models was attained by ANN at 66% and 62%, respectively, compared to full and reduced LSM at 40% and 34%, respectively. The average relative increase in accuracy over all in the full and reduced models is 82% and 102%, respectively. CONCLUSIONS: Using ANN modeling, we can predict salt reduction behaviors with 66% accuracy. The statistical model has been implemented in an online calculator and can be used in clinics to estimate the patient's behavior. This will help implementation in future research to further prove clinical utility of this tool to guide therapeutic salt reduction interventions in high cardiovascular risk individuals.
BACKGROUND: High dietary salt intake is directly linked to hypertension and cardiovascular diseases (CVDs). Predicting behaviors regarding salt intake habits is vital to guide interventions and increase their effectiveness. We aim to compare the accuracy of an artificial neural network (ANN) based tool that predicts behavior from key knowledge questions along with clinical data in a high cardiovascular risk cohort relative to the least square models (LSM) method. METHODS: We collected knowledge, attitude and behavior data on 115 patients. A behavior score was calculated to classify patients' behavior towards reducing salt intake. Accuracy comparison between ANN and regression analysis was calculated using the bootstrap technique with 200 iterations. RESULTS: Starting from a 69-item questionnaire, a reduced model was developed and included eight knowledge items found to result in the highest accuracy of 62% CI (58-67%). The best prediction accuracy in the full and reduced models was attained by ANN at 66% and 62%, respectively, compared to full and reduced LSM at 40% and 34%, respectively. The average relative increase in accuracy over all in the full and reduced models is 82% and 102%, respectively. CONCLUSIONS: Using ANN modeling, we can predict salt reduction behaviors with 66% accuracy. The statistical model has been implemented in an online calculator and can be used in clinics to estimate the patient's behavior. This will help implementation in future research to further prove clinical utility of this tool to guide therapeutic salt reduction interventions in high cardiovascular risk individuals.
Authors: Sophia Papadakis; Andrew L Pipe; Isabella A Moroz; Robert D Reid; Christopher M Blanchard; Danielle F Cote; Amy E Mark Journal: Can J Cardiol Date: 2010-05 Impact factor: 5.223
Authors: Y Friedlander; D S Siscovick; S Weinmann; M A Austin; B M Psaty; R N Lemaitre; P Arbogast; T E Raghunathan; L A Cobb Journal: Circulation Date: 1998-01-20 Impact factor: 29.690