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Article type: Research Article
Authors: Maryum, Ilsaa | Nawaz, Waqasb; * | Ud Din, Amadc
Affiliations: [a] Department of Computer Science, Fatima Jinnah Women University (FJWU), The Mall, Rawalpindi, Pakistan | [b] Department of Computer and Information Systems, Islamic University of Madinah, Prince Naif Ibn Abdulaziz, Al Madinah Al Munawwarah, KSA | [c] Department of Physics, Fatima Jinnah Women University (FJWU), The Mall, Rawalpindi, Pakistan
Correspondence: [*] Corresponding author. Waqas Nawaz, Department of Computer and Information Systems, Islamic University of Madinah, Prince Naif Ibn Abdulaziz, 3087-Al Jamiah Dist. Al Madinah Al Munawwarah, 42351 - 7969, KSA. E-mail: [email protected].
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.
Keywords: Fuzzy C-mean, association rule mining, hospital management society, intelligent system
DOI: 10.3233/JIFS-200349
Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7123-7134, 2020
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