Prediction of Chronic Kidney Disease - A Machine Learning Perspective

Abstract

Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis is required as soon as possible. Machine learning technique has become reliable for medical treatment. With the help of a machine learning classifier algorithms, the doctor can detect the disease on time. For this perspective, Chronic Kidney Disease prediction has been discussed in this paper. Chronic Kidney Disease dataset has been taken from the UCI repository. Seven classifier algorithms have been applied in this research such as artificial neural network, C5.0, Chi-square Automatic interaction detector, logistic regression, linear support vector machine with penalty L1 & with penalty L2 and random tree. The important feature selection technique was also applied to the dataset. For each classifier, the results have been computed based on (i) full features, (ii) correlation-based feature selection, (iii) Wrapper method feature selection, (iv) Least absolute shrinkage and selection operator regression, (v) synthetic minority over-sampling technique with least absolute shrinkage and selection operator regression selected features, (vi) synthetic minority over-sampling technique with full features. From the results, it is marked that LSVM with penalty L2 is giving the highest accuracy of 98.86% in synthetic minority over-sampling technique with full features. Along with accuracy, precision, recall, F-measure, area under the curve and GINI coefficient have been computed and compared results of various algorithms have been shown in the graph. Least absolute shrinkage and selection operator regression selected features with synthetic minority over-sampling technique gave the best after synthetic minority over-sampling technique with full features. In the synthetic minority over-sampling technique with least absolute shrinkage and selection operator selected features, again linear support vector machine gave the highest accuracy of 98.46%. Along with machine learning models one deep neural network has been applied on the same dataset and it has been noted that deep neural network achieved the highest accuracy of 99.6%.

Evaluating the impact of multivariate imputation by MICE in feature selection

“…Many studies have been carried out in order to explore mechanisms for handling missing values in different fields [5][6][7][8][9][10][11][12][13]. Although choosing the method may be difficult, most studies conclude that imputation is better than removing data due to the fact that deleting data could bias datasets as well as subsequent analyzes on these [14].…”

An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard Data

“…The feature weight will be high only when the feature is able to differentiate it among the instances of different classes and attain a similar weight for the same class instances. It was noted that the feature set selected by LASSO provided better performance when compared to the other two feature selection algorithms [23]. Hence in this work, features selected by LASSO are considered for further experimental analysis.…”

“…The feature extraction by multi labeled has grabbed attention and also shown significant progress in the selection of features. The least absolute shrinkage and selection operator (LASSO) [23] is a common high-dimensional data analysis technique that can perform feature selection and regularization simultaneously. LASSO can accomplish the selection of variables and regularization that can enhance the interpretation process and accuracy of prediction.…”

“…The Least Absolute Shrinkage and Selection Operator (LASSO) identify the relevant features by updating the coefficient of features, where the features are removed from the feature space only when the feature coefficient becomes zero [23]. Therefore, the features with greater coefficient values are added to the relevant features, and the remaining will be removed.…”

A Machine Learning Analysis of Health Records of Patients With Chronic Kidney Disease at Risk of Cardiovascular Disease

“…Polat et al [7] showed an application of a Support Vector Machine variant for patient classification to the same dataset. Chittora and colleagues [22] applied numerous machine learning classifiers and their variants for patient classification. Few studies published recently employed datasets different from the UC Irvine ML Repository one.…”