Assessment of Wellbeing in Diabetes- A Comparison of MLP with Back- propagation and Support Vector Regression

摘要: Diabetes Mellitus is a chronic metabolic disorder and managing of which difficult proposition in clinical setup. Assessing the wellbeing as an outcome treatment being increasingly recognized crucial component diabetic research. We have developed two prototype models one MLP Neural Network with back-propagation algorithm another support vector regression (SVR) model. Data was collected from cohort 200 individuals diabetes generated real life. used age, gender, weight, fasting blood sugar levels (FBS) bias set inputs assessed measures well-being by considering four each depression, anxiety, energy positive outputs. Similarly, we predicted relationship between weight versus three single output above variables input gender bias. Females report higher depression not much difference anxiety has been observed regard to male counterparts. Males slightly more well-being(PWB) values compared females. experience PWB respect weight. SVR assess psychosocial parameter when biological biographical were given networks. It that, Although, both similar results their error values, preferable gives lower for assessing diabetes.