Prediction Model by Using Bayesian and Cognition-driven Techniques: A Study in the Context of Obstructive Sleep Apnea

This research proposes a mechanism for cost-effective medical diagnostic support for relatively new physical ailments or diseases where there are incomplete data sets available and hence, common parameters are forced to be used for drawing a- priori inferences. We propose a simple but powerful prediction model that combines the advantages of the Bayesian Approaches and Cognition-Driven Techniques such as Expert Reasoning (ER) and Cognitive Reasoning (CR) using Markov Chain analyses. Then, we demonstrate the effectiveness of our approach in predicting Obstructive Sleep Apnea (OSA).