TY  - JOUR
T1  - Forecasting Chronic Kidney Disease Stages and Urgency level of Dialysis Using Time Series
Algorithm
AU - N. Balute, August Anthony AU - B. Gonzales, Dennis AU - T. Carpio, Jennifer AU - A. Vinluan, Albert 
JO  - Agricultural Journal
VL  - 15
IS  - 6
SP  - 143
EP  - 148
PY  - 2020
DA  - 2001/08/19
SN  - 1816-9155
DO  - aj.2020.143.148
UR  - https://makhillpublications.co/view-article.php?doi=aj.2020.143.148
KW  - Acute Renal Failure (ARF)
KW  -ARIMA model
KW  -Time Series algorithm
KW  -Chronic Kidney Disease (CKD)
KW  -Electronic Medical Record (EMR)
KW  -R programming
KW  -scrum methodology
AB  - 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.
ER  -