Chronic kidney disease (CKD) is an increasing and serious disease impacting public health worldwide. The symptoms of CKD are often appearing too late and many patients inevitably face pain and expensive medical treatments. The ultimate treatment is frequent dialysis or Kidney transplant. Early detection of disease through symptoms can prevent the disease progression by referral to appropriate health care services. Machine Learning (ML) techniques can help in identifying the potential risk by discovering knowledge from medical reports of patient. Thus helps in preventing the disease progression. Several models for detecting the risk of CKD, proposed in the literature are based on Data Mining (DM) techniques like classification, clustering and regression etc. These models are demonstrated using variety of languages like Python, Java and tools like Weka and RapidMiner.This research aims at developing a Cloud based Predictive Model to detect the possibilities of CKD and its progression in patients with some health issues like hypertension and diabetes.

Chronic Kidney Disease (CKD), Health Care, Microsoft Azure, Logistic Regression, Machine Learning, Predictive Model

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