Diagnosis of Chronic Kidney Disease using Naïve Bayes algorithm Supported by Stage Prediction using eGFR
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Abstract
The proliferation of data and availability of open source tools has simplified the diagnosis of diseases such as CKD (Chronic Kidney Disease). As one of the types of kidney disease which results in malfunctioning of kidney, it is paramount to effectively diagnose such diseases to prevent degeneration of vital organs in the body. Despite the advancements in the field of medical imaging, there exists no permanent cure for CKD, but the risk can be mitigated to a larger extent if detected at the early stage. This paper proposes a hybrid approach to early detection of chronic kidney disease by using Naïve Bayes classifier and eGFR (estimated Glomerular Filtration Rate). Naïve Bayes which works on the principle of conditional probability was used to predict whether a patient has CKD or not based on clinical symptoms, and the stage was determined using the eGFR formula. Results were promising as the model was able to predict the prevalence of CKD as well as the stage in which the patient was in. Although we were able to develop a web-based application using machine learning algorithms to aid in the diagnosis of CKD by serving as a “self diagnostic” tool for medical practitioners, improvements could be made to ensure that the model works according to established ground truth by nephrologists.
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