Cadaver Kidney Demand Forecasting and Classification Modelling of Kidney Allocation–A Case Study. (2016)

• Record Type: Journal Article
• Authors: Tom, Pius; Kumar, K. Sunil
• Is Part Of: Procedia technology. Volume 25(2016)
• Page Start: 1162
• Page End: 1169
• Publication Date: 2016
• Subjects: Cadaver kidney transplantation -- Forecasting -- Classification -- Kidney allocation; Technology -- Congresses; Technology -- Periodicals; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Technology; Conference proceedings; Periodicals; 605
• DOI: 10.1016/j.protcy.2016.08.234
• Languages: English
• ISSNs: 2212-0173

Abstract

Abstract: Cadaver kidney transplantation is one of the most promising treatment options available to end stage renal disease patients. However there has been a lot of ethical, operational and medical issues centered on the waiting list and kidney allocation. The initial phase of the study uses time series forecasting techniques to addresses the growth and style of cadaver kidney demand in a variety of scenarios. The second phase deals with the kidney allocation, characterizing the demarcation between met demand (allocated) and unmet demand (not allocated) using classification technique, a prominent tool of data mining. It is observed from the study that the overall demand for kidney shows an increasing linear trend in the future. It is also seen that aged patients, patients with blood group O etc. poses higher risk of non-allocation. The study highlights the need for a comprehensive and holistic system for kidney allocation.