Diagnosing Chronic Kidney Disease Using Hybrid Machine Learning Techniques
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Abstract
The chronic kidney failure is a serious health issue and if not detected and treated at the early stages, it can be very
deadly. Hence the major objective of this paper is to develop a reliable machine learning model which predicts the CKD with a
high accuracy rate. The CKD data set is downloaded from the famous UCI ML repository but it suffers from a lot of missing
values. To handle the missing values KNN Imputation is used. Feature selection is also performed with the help of information
gain as the dataset is huge and hence the cost of modelling can be very high. Various other pre-processing steps like label
encoding and Min-max normalization is performed to attain a clean dataset. After pre-processing, various ML algorithms like
logistic regression, naïve bayes, artificial neural network and random forest are applied and their performances are compared
with the help of various performance metrics. A hybrid of Random Forest and Adaboost algorithm is proposed and it achieves
a better accuracy when compared to the other individual component models and hence it can be proved that the proposed
hybrid model is much better and accurate in diagnosing CKD.
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