Predictive modeling for wellness and chronic conditions
There is a significant increase in attention being paid to personal wellness as a preventative strategy in healthcare. At the same time, chronic diseases are the major cause of mortality, accounting for 7 out of 10 deaths in the United States. Healthcare costs involved in managing chronic diseases are also very high. So there is a need to help better maintain individual wellness, as well as better manage chronic conditions. Predictive analytics based clinical decision support systems need to be developed to help individuals and healthcare providers to better manage wellness or chronic conditions. In this paper, we investigate two different classifiers to predict the wellness outcome and the occurrence of a chronic condition (diabetes). The models were evaluated on the basis of overall accuracy, root mean squared error and Area under ROC. National CDC-NHANES data that is based on the health and nutritional status of individuals in the United States is used to develop the models.
