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. 2019 Jul 3;9(1):9583.
doi: 10.1038/s41598-019-46074-2.

Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease

Mohamed Elhoseny  1 , K Shankar  2 , J Uthayakumar  3
Affiliations

Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease

Mohamed Elhoseny et al. Sci Rep. .

Erratum in

Abstract

At present times, healthcare systems are updated with advanced capabilities like machine learning (ML), data mining and artificial intelligence to offer human with more intelligent and expert healthcare services. This paper introduces an intelligent prediction and classification system for healthcare, namely Density based Feature Selection (DFS) with Ant Colony based Optimization (D-ACO) algorithm for chronic kidney disease (CKD). The proposed intelligent system eliminates irrelevant or redundant features by DFS in prior to the ACO based classifier construction. The proposed D-ACO framework three phases namely preprocessing, Feature Selection (FS) and classification. Furthermore, the D-ACO algorithm is tested using benchmark CKD dataset and the performance are investigated based on different evaluation factors. Comparing the D-ACO algorithm with existing methods, the presented intelligent system outperformed the other methodologies with a significant improvisation in classification accuracy using fewer features.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Block diagram of D-ACO algorithm.
Figure 2
Figure 2
Flowchart of the D-ACO method.
Algorithm I
Algorithm I
Density based feature selection with Ant Colony Optimization (D-ACO) for Data Classification
Figure 3
Figure 3
Structural schema of ACO.
Figure 4
Figure 4
Sample frequency distribution of 24 features.
Figure 5
Figure 5
Sample class distribution of 24 features.
Figure 6
Figure 6
Features that influence on CKD.
Figure 7
Figure 7
Performance measures. Where, Observed Agreement (O.A) = % (Overall Accuracy). Expected Agreement (E.A) = (% (TP + FP) *% (TP + FN)) + (% (FN + TN)* %(FP + TN)).
Figure 8
Figure 8
(a) Number of chosen features under ten iterations and (b) comparing Accuracy of chosen features over ten iterations.
Figure 9
Figure 9
Comparative results of different classifiers in terms of (a) FNR and (b) FPR.
Figure 10
Figure 10
Comparison of various classifiers interms of (a) Sensitivity (b) Specificity (c) F-Score (d) Kappa.
Figure 11
Figure 11
Comparison of various classifiers interms of accuracy.
See this image and copyright information in PMC

References

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