CHRONIC KIDNEY DISEASE STAGE IDENTIFICATION HIV INFECTED PATIENTS USING MACHINE LEARNING

Abstract

Chronic Kidney Disease (CKD) is one of
worldwide medical challenges with high
morbidity and death rate. Since there is no
symptom during the early stages of CKD,
patients often fail to diagnose the disease.
Patients with HIV have more chances to be
affected with CKD in critical condition.
Early detection of CKD helps patients to
obtain prompt care ald delays the further
progression of disease. With the availability
of pathology data, the use of machinelearning techniques in healthcare for
classification and prediction of disease has
become more common. This paper presents
the classification of CKD using machine
learning models. Based on the glomerular
filtration rate, the CKD stages are also
calculated for patients diagnosed with CKD.
DNN model outperforms with 99% of
HIV