Predictive Analytics of Chronic Kidney Disease using Machine Learning Algorithm
S. Pitchumani Angayarkanni

Dr. S. Pitchumani Angayarkanni, Associate Professor, Department of Computer Science, Lady Doak College, Madurai, (Tamil Nadu), India.
Manuscript received on 11 March 2019 | Revised Manuscript received on 19 March 2019 | Manuscript published on 30 July 2019 | PP: 940-947 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1727078219/19©BEIESP | DOI: 10.35940/ijrte.B1727.078219
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Abstract: According to the health statistics of India on Chronic Kidney Disease (CKD) a total of 63538 cases has been registered. Average age of men and women prone to kidney disease lies in the range of 48 to 70 years. CKD is more prevalent among male than among female. India ranks 17th position in CKD during 2015[1]. This paper focus on the predictive analytics architecture to analyse CKD dataset using feature engineering and classification algorithm. The proposed model incorporates techniques to validate the feasibility of the data points used for analysis. The main focus of this research work is to analyze the dataset of chronic kidney failure and perform the classification of CKD and Non CKD cases. The feasibility of the proposed dataset is determined through the Learning curve performance. The features which play a vital role in classification are determined using sequential forward selection algorithm. The training dataset with the selected features is fed into various classifier to determine which classifier plays a vital and accurate role in detection of CKD. The proposed dataset is classified using various Classification algorithms like Linear Regression(LR), Linear Discriminant Analysis(LDA), K-Nearest Neighbour(KNN), Classification and Regression Tree(CART), Naive Bayes(NB), Support Vector Machine(SVM), Random Forest(RF), eXtreme Gradient Boosting(XGBoost) and Ada Boost Regressor (ABR). It was found that for the given CKD dataset with 25 attributes of 11 Numeric and 14 Nominal the following classifier like LR, LDA, CART,NB,RF,XGB and ABR provides an accuracy ranging from 98% to 100% . The proposed architecture validates the dataset against the thumb rule when working with less number of data points used for classification and the classifier is validated against under fit, over fit conditions. The performance of the classifier is evaluated using accuracy and F-Score. The proposed architecture indicates that LR, RF and ABR provides a very high accuracy and F-Score.
Index Terms: Chronic Kidney Disease, Classifier, Logistic Regression, Ada Boost Regressor and Performance metrics..

Scope of the Article: Machine Learning