Agricultural Journal
ISSN: Online 1994-4616ISSN: Print 1816-9155
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Abstract
As medical data may contain diagnoses and treatments that are subject to error rates, imprecision and uncertainty, medical data mining methods and tools require medical research using data mining methods and artificial intelligence techniques to systematically come up with a suited analysis of the medical database. A Time Series algorithm specifically Auto Regressive Integrated Moving Average (ARIMA) model can be used to detect and analyze frequency and probability of data by assessing essential attributes to predict and forecast trends which in this study is predicting the urgency of dialysis and Chronic Kidney Disease (CKD) stage, to determine the urgency level and prioritization of selected kidney patients. As a valuable tool for predicting future health events demanding services and healthcare needs, preventive measures and intervention strategies will be recommended by doctors easily for decision-making support using a time series algorithm.
How to cite this article:
August Anthony N. Balute, Dennis B. Gonzales, Jennifer T. Carpio and Albert A. Vinluan. Forecasting Chronic Kidney Disease Stages and Urgency level of Dialysis Using Time Series
Algorithm.
DOI: https://doi.org/10.36478/aj.2020.143.148
URL: https://www.makhillpublications.co/view-article/1816-9155/aj.2020.143.148