Hospital management society: A framework based on fuzzy logic and association rule mining towards well-being society

Abstract

Non-uniformity in medical procedures, expensive medical treatments, and the shortage of medicines in different areas are health care problems in our country. This paper aims to resolve that problem by developing a web-based-application called Hospital Management Society (HMS) based on a novel Dynamic Optimized Fuzzy C-mean Clustering and Association Rule Mining (DOFCCARM). The purpose of HMS is to enhance the hospitals (and clinics) by regulating, overseeing and accrediting them to bring uniformity in health care facilities, to make the medical treatment cost effective, to find common diseases in a particular age and area, and to help government in identifying the areas facing the shortage of licensed medicines. Therefore, HMS creates a single platform for both the doctors of central hospital (CH) and the doctors of member hospitals (MH). The CH provides clinical practice guidelines for various diseases. A team of doctors at CH evaluate the medical treatment provided by MH. If a hospital fails to maintain the standard then HMS blacklists such hospital. In our approach, we take a range of values to distinct successive partitions and generate a parallel membership function to make fuzzy sets of patients report, rather than single partitioning point. We determine the effectiveness of our approach through experiments on a dataset. The results revealed the most common age, symptoms and location for a particular disease and shortage of particular medicine in a specific area.