Sickle Cell Disease (SCD) is the most common single-gene disorder worldwide and has multiple and variable manifestations. The many medical complications associated with it such as acute chest syndrome and painful crises, along with a lack of normal functioning, may lead to various psychosocial problems such as depression, loneliness and impaired quality of life. A few studies have sought to examine the relationships between demographics, disease severity, depression, loneliness and quality of life of such patients. In this paper we apply the knowledge discovery via data mining (KDDM) process to explore factors which impact the quality of life of sickle cell patients in Jamaica to explicate knowledge which can be used by medical professionals. We use multiple modeling techniques such as Decision Trees, Regression and Regression splines to generate multiple models on the dataset and then present a best set of models to the medical professionals. This allows the medical professionals to select models which will assist them in the decision making process. The benefits of using the process model are highlighted in this study.