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Detecting chronic kidney disease by using a voting classifier


 
 
Chronic Kidney Disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. Since there are no obvious symptoms during the early stage of CKD, patients often fail to notice the disease. Early detection of CKD enables patients to receive timely treatment to ameliorate the progression of this disease. Machine learning models can effectively aid clinicians achieve this goal due to their fast and accurate recognition performance. In this, we proposed a machine learning methodology for diagnosing CKD. The CKD data set was obtained from the University of California Irvine (UCI) machine learning repository, which has a large number of missing values. In general we have six machine learning algorithms like logistic regression, random forest, support vector machine, k-nearest neighbour, naive Bayes, classifier and feed forward neural network to establish models. In existing system random forest achieved the performance with 99.75% diagnosis accuracy. In proposed system, By analysing the misjudgments generated by the above model, we proposed an integrated model that combines logistic regression and random forest by using perceptron which could achieve an efficient accuracy of 99.83% after ten times of simulation. Hence, we speculated that this methodology could be applicable to more complicated clinical data for disease diagnosis. 
 
 
