Classifying the Status of CKD Using Randomized Weighted Optimization Model Using Machine Learning Techniques

chronic kidney disease (CKD) is a substantial health-care burden owing to its growing occurrence, enhanced
danger of development to end-stage renal disease, and reduced mortality and morbidity prognosis. It is
quickly accelerating to a worldwide well-being catastrophe. The main causes of CKD include high blood
pressure and diabetes. The major function of the kidney is filtering waste from a human body. When kidneys
fail, waste builds up in bodies and eventually leads to death. Researchers all around the globe utilize the
Glomerular Filtration Rate (GFR) and kidney damage indicators to define CKD as a disorder that causes
decreasing renal function over a period of time. A person having CKD is more likely to die early. Doctors
have a tough time recognizing the several disorders associated with CKD early enough to avoid the
condition. Data mining approaches have recently been exposed to considerable research in CKD diagnosis,
with a focus on accuracy, either through the simplicity of illness by conducting feature selection in addition
to pre-processing or not before classification. This research work determines creatinine level is a kind of
blood metabolite that has a significant relationship with GFR and Blood Urea Nitrogen (BUN) BUN
Creatinine Ratio (BCR). Since measuring GFR and BCR is challenging, this work focused in analysing
creatinine value is used to determine Estimated GFR (EGFR) and BCR from serum creatinine indirectly
utilizing the attributes available in the dataset whereas the 28 attributes are considered inclusive of “Gender”
with 523 records. The EGFR and BCR computing results are used in this prediction analysis for providing
an accurately categorizing the status of CKD which has been utilized by Randomized Weighted
Optimization Model (RWOM). Furthermore, the proposed RWOM with Neural Network (NN) is compared
to the RWOM with Logistic Regression (LR), NN, LR in terms of analysing the better categorization status
of CKD from patient recor