Classification of chronic kidney disease (ckd) using data mining techniques
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In the past decade rapid growth of digital data and global accessibility of it through modern internet has seen a massive rise in machine learning research. In proportion to it, the medical data has also seen a massive serge of expansion. With the availability of structured clinical data, it has attracted scores of researchers to study on the automation of clinical disease detection with machine learning and data mining. Chronic Kidney disease (CKD) also known as renal disorder has been such a field of study for quite some time now. So, our research aims to study the automated detection of chronic kidney disease with clinical data using several machine learning classifier. This research particularly focuses on Random Forest classifier, Naïve Bayes and decision tree in the purpose of classifying the intended dataset. Observational and comparative studies will be conducted on the each of the classifier’s accuracy. The correlation and importance of each of the attributes to achieve the intended classification has been also explored in this study. Overall our endeavor has been to achieve a sustainable and feasible model to detect the chronic kidney disease with comprehensive clinical accuracy.