Estimating the mortality rate using statistical variance and reduced set of clinical and non-clinical attributes for diagnosing chronic kidney disease

Abstract

It is found that, chronic kidney disease (CKD) is prevalence worldwide. Quality of life (QoL) in terms of health became an essential measure for patients with CKD. This paper uses the real-time dataset of CKD patients collected from reputed medical dialysis unit in Chennai, India. We measure the inter and intra class variations between the clinical and non clinical attributes. Principal component analysis (PCA) is applied on twelve clinical (biomarkers) and eight non-clinical (comorbiditity) attributes to find salient among them. ANOVA is applied only on reduced attributes to calculate the correlation between the target variables such as mortality, age and gender. The characteristics of the attributes and its discriminating nature is evaluated using various well known classifiers such as Logistic Regression, K-nearest neighbor, support vector machine, Neive Bayes, decision tree, random forest and artificial neural network. The performance of the classifiers are evaluated using parameters such as confusion matrix, accuracy, F-measure, precision and recall. It is found that, the covariance of the attributes linearly separates the output space of target variables that are considered and the performance is encouraged