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R. Subhashini*et al. /International Journal of Pharmacy & Technology

IJPT| Dec-2017| Vol. 9 | Issue No.4 | 6563-6582 Page 6563

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CODEN: IJPTFI

Available Online through Review Article

www.ijptonline.com PERFORMANCE ANALYSIS OF DIFFERENT CLASSIFICATION TECHNIQUES

FOR THE PREDICTION OF CHRONIC KIDNEY DISEASE

R. Subhashini*1

, M.K.Jeyakumar2

*1Research Scholar, Department of Computer Science, Noorul Islam Center for Higher Education,

Kanyakumari, Tamil Nadu, India 629180. 2Additional Controller of Examinations, Noorul Islam Center for Higher Education,

Kanyakumari, Tamil Nadu, India 629180.

Email: [email protected]

Received on 28-10-2017 Accepted on: 25-11-2017

Abstract

The performance degradation of renal of human being is portrayed as chronic kidney disease. Itdisappearthe

human life by causing severe health issues thus its prediction becomes a central attraction in medical and

research field. This paperanalyses number of existing literatures which are utilisingdiverse techniques

principally classification algorithms for predicting chronic kidney disease. The performance analysis is carried

out for the artificial neural network, naive Bayes, support vector machine, decision tree, k-nearest neighbour

and fuzzy classifiers. The performance of these algorithms is evaluated using fiveclassification parameters. The

classification methodologies analysed in this paper are derived from three major strategies they are the neural

network, statistical methods and machine learning. Therefore by analysing the six techniques, this paper

outlines the deep study of different classification strategies with its performance. Finally performance analysis

is evaluated for the medical data set and comparison is graphically shown with the MATLAB simulation tool.

Keywords: Chronic kidney disease; classification; artificial neural network; k-nearest neighbour; decision tree;

naive Bayes.

1. Introduction

Chronic kidney disease is the term for heterogeneous disorders which affect the functions and structure of

kidney by doing damage or decreasing the functions. In developed country, this disease has associated with

diabetes, old age, obesity, and hypertension, etc. So the exact diagnosis is tough. The abnormal presence of red

and white blood cells causes the disease. The stage of the disease is based on the glomerular filtration rate

(GFR). In normal the genetic factor can lead to the disease. If it increases, it causes damage to the kidney, after

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that the glomerular filtration rate is defined at a higher rate, which causes the kidney failure. This can be

diagnosed by the dialysis or transplantation technology. While providing this treatment the patients‟ health may

be improved or else the disease gets worse and leads to death. [1-3].

The injuries and burden of disease measurement are the input of human health policy. The entire group of

chronic disease can be degraded by diminishing the heart disease and diabetes. The common health concern is

the excess of body weight [4-5]. The enzyme method is presented to estimate the glomerular filtration rate. The

metabolism of vitamin D is regulated by the growth factor of fibroblast 23 (FGF23). The binding of co-

receptors can yield the glands of parathyroid cellular specificity in the kidney which improves the affinity of

growth factor to express the FGF receptors. The maintenance of phosphate balance in the disease affected

peoples with the compensatory response is the FGF level of 23 [6-7].

The chronic kidney disease can be predicted by several methods among this the abbreviated Modification of

diet in renal disease study equations which is modified by the coefficients of Japanese is the one method to

catch the disease. Thought out this technique the GFR values are calculated from the value of serum creatinine.

However, the renal function never decreased with a lower value of GFR [8]. Then the model of consecutive

series is developed to the disease with several variables which are independent of kidney failure. The model can

use the data's of sex and age, adding clinical and candidate variables. The hazard ratio is fitter for successive

models, but it is poor. The disease of CKD with patients may lose their energy and protein. The term of

cachexia represents a lot of protein- energy wastage [9].

CKD predicts one of the risk factors of the cardiovascular event. There are no equations established to prevent

the risk in CKD patients. The predicted scores are required to beat the risk by the evaluation of power [10]. The

CKD is based on the GFR, albuminuria, and GFR. The disease can be avoided by stopping the drug usage,

better treatment, test and intervention [11]. The level of higher metabolite levels depends on the decreased risk

of CKD. In early stages disease never provides the information about the injury and the cause of disease. The

patients will intake the optimal amount of energy [12-13]. The pathway of wnt/β-catenin and redox signalling

are used to avoid disease and five urine metabolites are correlated with GFR [14].

The cardiac surgery can treat the kidney injury, and the intraoperative variables can improve the current clinical

score. The prediction tools are used to handle the surveillance and Reno-protective efforts [15-16]. The risk

factors are in the disease in which the proteinuria is the important one, and it can be avoided by changing the



intraglomerular hemodynamic. This will be independent of human blood pressure. The CV (cardiovascular)

risks are not only focusing on the clinical decision making but also make the health care. The biomarkers are

important to clear the pathway and to determine the injury in the kidney [17-18].

The most of the patients with chronic kidney disease are associated with the successive problems such as

diabetes, hypertension and cardiovascular disease [19-20]. Therefore the early prediction of chronic kidney

disease may save the human life by controlling the effects of disease and cost of treatment. The major

contribution of this review is analysing CKD prediction techniques with their accuracy of detection.

The recently growing health hindrance across the universe is the Chronic Kidney Disease (CKD). Table 1

defines the growth rate of different countries [21].

Table 1. Disease growth rate of countries.

No. Country Growth (out of 100 people)

1 The United States of America 10 or 15

2 Australia 11.2

3 Singapore 10.1

4 Japan 18.7

5 Iran 8.3 to 18.9

6 India 16

Table 2. Comparison of KNN performance.

Author(s) Technique Advantage Accuracy

(%)

Parul Sinha and

Poonam Sinha

[42]

KNN

SVM

It provides a new decision-

making system for

predicting CKD.

78

73

Ani R et al. [43] neural network based

back propagation (BPN)

probability based Naive

Bayes

Helps to improve the

accuracy and ease of

diagnosis.

81.5

78

76



LDA classifier

BasmaBoukenze,

et al. [44]

C4.5

KNN

NB

MLP

Supports health care

industry to take decisions

for diagnosing kidney

failure.

63

58.25

57.5

62.25

Table 3. Attributes.

age - age

bp - blood pressure

sg - specific gravity

al - albumin

su - sugar

rbc - red blood cells

pc - pus cell

pcc - pus cell clumps

ba - bacteria

bgr - blood glucose random

bu - blood urea

sc - serum creatinine

sod - sodium

pot - potassium

hemo - haemoglobin

pcv - packed cell volume

wc - white blood cell count

rc - Red blood cell count

htn - hypertension

dm - diabetes mellitus



cad - coronary artery

disease

appet - appetite

pe - pedal edema

ane - anemia

The morbidity and mortality have been an increasing problem in the medical research, which is greatly caused

by CKD [22]. The major contribution of this review is analysing CKD prediction techniques with their

accuracy of detection.

This paper is further organized as follows: Section 2, Section 3 and Section 4 presents the motivation of the

work, Methodology involved in the prediction of CKD, result and discussion with performance metrics.

Furthermore, Section 5 includes the conclusion, and finally references are added.

2. Methodology involved in the prediction of CKD

The healthcare sector in each country handles a large amount of data about the health condition of people.

However, the information from the data is not well used for the analysis and prediction of diseases. In the past

literature works these kinds of datasets are processed with some techniques, and the accurate prediction of the

disease has attained. The techniques have its pros and cons. The five most used methods in the prediction are

evaluated in this work. Figure 1 describes the flow of the work.

Figure 1.Classification of Chronic kidney disease.


IJPT| Dec-2017| Vol. 9 | Issue No.4 | 6563-6582 568

3.1. Dataset Collection

The dataset for the analysis is collected from the UCI Machine Learning Repository. The dataset of original

version has multiple different entries without some of the deleted or unfilled data. The collection has 400

instances for 24 attributes in which 11 attributes have numerical values, and 13 have nominal values. These

instances are divided into 250 identified as chronic kidney diseases and 150 as not chronic kidney diseases.

3.2. Classification Techniques

The regression, classification, clustering and association based techniques have been utilized for the prediction

of the diseases [23]. In this analysis, there are five classification techniques discussed for the prediction

accuracy. They are an artificial neural network (ANN), K-nearest neighbour (KNN), Support Vector Machine

(SVM), Naive Bayes, Decision Tree and fuzzy.

3.2.1. Artificial Neural network based classification

Nowadays the neural network is employed in the field of medical as a classifier to classify cancer [24],

diagnosis of Parkinson [25] disease and heart disease. Figure 3 demonstrates the ANN based classification.

The widespread usage of the neural network as a pattern classifier increases, because it learns the input data and

provides the desired response based on that data [26]. The error correction based training relates the preferred

output and input data as,

nYnDnE iii (1)

In equation (1) i represents the neuron, n is the number of iteration, nE i is the error response, nDi is the

preferred output and nY i is the estimated response.

In a neural network for every number of input attributes the comparison is the main task in the output side [27]

[28]. SherifE.Husseinet al. [29] confess iridology validation for estimating the kidney aberrations. This was

validated through the classification by combined wavelet and neural network analysis. Chongjian Wang et al.

[30] had proposed ANN as an efficient classification approach for the prediction of type 2 diabetes mellitus

affected grown-ups.

The processing way carried this methodology without using any of the biochemical parameters.

JamshidNorouziet al. [19] acknowledged a Neuro-fuzzy interference system for predicting renal failure based

on the real-time data.



3.2.2. K-Nearest Neighbour based classification

The K- Nearest neighbour is the straight forward classifier with greater accuracy. The classification in this

algorithm depends upon the similarity measure. The continuity of attributes is very much important in this

process [31]. The progression stages in the nearest neighbour algorithm are shown in figure 2.

Figure 2. Flow of prediction methodology.

Ani R et al. [32] were utilizes the neural network based back propagation (BPN), probability based Naive

Bayes, LDA classifier techniques for the prediction with 81.5, 78 and 76% accuracy respectively.

BasmaBoukenze, et al. [33] was used C4.5, KNN, NB and MLP with 63, 58.25, 57.5 and 62.25% accuracy

respectively.

3.2.3. Support vector machine based classification

The SVM has considered as a machine learning algorithm for the classification of two-class problems. The

training of support vector machines with the positive and negative types of data is known as one-against-all or

one-against-rest. The two-class type SVM is combined for creating a multi-class support vector machine. The

classification by this algorithm is represented as in the equation [34] (2).

bxF j

n

jjj ,sgn

1 (2)

In (2) j is the Lagrange Multiplier, jj xand are the dimensional vectors of the two associated classes and

j , is the Kernel function. The numerical difficulties of this method are eliminated using Kernel function.



Davar Givekiet al. [35] investigated feature weighted support vector machines and modified cuckoo search

based technique for evaluating the diabetes disease. It was processed with UCI dataset and provided an

accuracy value of 93.58%. Abdullah A. Aljumahet al. [36] had provided an experimental verification for

diabetes cure analysis regression tree based data mining techniques such as support vector machine. The

multiple ensemble based classifier analysis of cardiovascular disease had discussed in [37] by Jae-Hong Eomet

al. By analyzing the urinary biomarkers of patients, Wolfram Gronwald, et al. [38] had predicted the kidney

function let-down. In this analysis, the SVM classifier was used along with multidimensional-multinuclear

nuclear magnetic resonance spectroscopy.

3.2.4. Naive Bayes based classification

Consider n number of classes nNNN ,........, 21 . The each single sample is constructed with a vector S of dimension

with i number of attributes. The naive Bayesian classifier uses the probability count for predicting the class of S

By this theorem,

SP

NPNSP

SNPii

i

(3)

For evaluating the numerator term, the attributes of the dimensional vector is considered as independent values

of each other. This is denoted as in (4).

p

PiPi NSPNSP

1

(4)

Ashok Kumar Dwivedi [39] had presented an analysis of six techniques for the early prediction of Diabetes

including naïve Bayes. The objective of the work was developed by Daria Prilutskyet al. in[40] is the

classification of infectious diseases by the data mining techniques with the lower accuracy of naïve Bayes

technique. Abeer Y. Al-Hyariet al. [41] had discussed a new clinical decision support system with classifiers

like naïve Bayes for envisaging the evolution of chronic renal failure with high accuracy. Asif Salekinet al. [42]

had addressed the CKD prediction with machine learning approaches 24 predictive parameters. The approaches

proposed in the paper were well designed for the diminution of noise data.

3.2.5. Decision tree based classification

This technique is a branching methodology which provides the outputs which are possibly related to the

decision. For the generation of proper decision tree approach uses greedy search. The classification tree



analysis was discussed by Paola Rucciet al. [43] for the estimation of clinical and demographic features of

patients who are holding differential annual decline in glomerular filtration rate to predict the chronic kidney

disease. The decision support methodology for the reduction of treatment cost and welfare of the person having

unwanted health behaviour affected with the problem of haemodialysis was presented by Jinn-Yi Yehet al.

[44]. Naoki Hiramatsuet al. [45] had used the decision tree analysis for identifying the anaemia disease. The

data was collected from the hepatitis C patients for the evaluation. The collection of data mining techniques

including decision trees is analysed for the accurate prediction of kidney diseases which helps the health

industry was reviewed by Ms. AsthaAmeta and Ms.Kalpana Jain in [46].

3.2.6. Fuzzy based classification

The fuzzy classifier can classify the object which holds multiple values. It provides a low value of

computational cost at the same it yields higher classification accuracy. In the fuzzy based classification

methodology, the data is converted into a practical form using the principal component analysis scenario [47]. It

provides some merits which are shown below [48].

1) Describing the knowledge using linguistic terms such as „„high” instead of hard terms.

2) Acting as simulating the actual situations.

N. Pavithra and Dr R. Shanmugavadivu have proposed a paper in which the data mining techniques including

fuzzy was analysed for the prediction of kidney disorder in the earlier stage of the disease. The methodology

was provided with the advantage of classification accuracy increment while increasing the attributes [49].

Jamshid Norouziet al. [19] were addressed intelligent fuzzy expert system for the prediction of chronic kidney

disease. The renal failure progression was identified in this paper as a major theme. This suggests the accurate

discovery over time is necessary for the reduction of cost and mortality rate. Mohammad SaberIraji [50] was

presented the fuzzy technique which combined with the neural network for the prediction of chronic kidney

disease. There are 24 features were considered for the evaluation.

4. Result and Discussion

The analyses of considered methods in the review are validated using Matlab R2016a installed in the system

having the configuration of Windows 10. The five kinds of methodologies namely ANN, KNN, Decision tree,

Naive Bayes, SVM and fuzzy are validated and tested for the classification accuracy of the prediction of



chronic kidney diseases. The parameters used for the validation are accuracy, precision, recall, specificity and

F-Measure.

4.1. Accuracy

The accuracy of the classifiers is measured by the equation (5).

negativefalsenegativetruepositivefalsepositivetrue

negativetruepositivetrueAccuracy

(5)

Figure 3. Neural network classification.

Figure 3 provides the classification accuracy of the techniques analysed here. Based on the accuracy analysis,

the fuzzy is highlighted by its performance. It provides a classification accuracy of 90%. The descending order

of performance is Fuzzy, Decision tree, KNN, Naïve Bayes, SVM and ANN. Even though the decision tree

technique exhibits larger searching time for the correct decision, it provides accurate output than the Naïve

Bayes and supports vector machine classifier.

4.2. Precision

Precision is the measure defined by the ratio of true positive to the addition of true positive and false positive. It

can be estimated using (6) and shown in figure 4.

positivefalsepositivetrue

positivetrueecision

Pr (6)



The fuzzy classifier provides the precision of 0.75. The ANN classifier provides the accuracy of 75% with a

precision of 0.37. The precision for the nearest neighbour for the accuracy of 85.5% is 0.37. The decision tree

method holds a precision value of 0.25 with accuracy 88.12%. The naïve Bayes and SVM has an accuracy of 85

and 82.5% with precision .451 and 0.412 respectively.

Figure 4. KNN classification.

4.3. Recall

The recall is another measure for the performance evaluation of classifiers, and it is otherwise known as

sensitivity. It is evaluated as by below equation (7).

negativefalsepositivetrue

positivetrueySensitivitcall

/Re (7)

In (7) true positive is the indication of the positive sample which is identified properly by the classification

technique used.The false negative is evaluated by positive sample mistakenly identified as the negative sample

by the classifier. The below figure 5 shows the performance of six classifiers for the parameter Recall.

Figure 5. Decision tree classification.



4.4. Specificity

Here we are taking specificity as the final measure for the analysis of the performance of the five classifiers

used in this work. The specificity can be evaluated using the equation (8).

positivefalsenegativetrue

negativetrueySpecificit

(8)

In (8) true negative is the identification of negative sample equally by the classification technique used.The

false positive is evaluated by negative sample mistakenly identified as the positive sample.

Figure 6. Accuracy of classifiers.

From figure 6 specificity value achieved by classifier support vector machine is 0.4125, and the naïve Bayes

technique is 0.456. The other classifiers ANN, KNN and decision tree used in the analysis provide the value of

0.6, 0.37 and 0.72 respectively. The specificity of the fuzzy classifier is 0.82.

4.5. F-measure

The F-measure is a parameter related to precision and sensitivity and for the proposed classifiers it is

represented in figure 7. The F-measure can be defined as,

senitivityprecision

senitivityprecisionmeasureF

2 (9)

Figure 7. Precision of classifiers.



Figure 8. Recall of classifiers.

Figure 9. Specificity of classifiers.

Figure10. MCC of classifiers.

Figure 11. NPV of classifiers.



Figure 12. F-measure of classifiers.

Figure 13. FDR of classifiers.

The F-Measure of the classifier is nearest to 1 means it is the highest performance classification methodology,

and the value is nearest to zero it indicates the stumpy performance. The F-measure of the fuzzy is 0.71, and the

decision tree classifier is 0.14 from the evaluation.

So the analysis for the prediction of chronic kidney diseases with classification techniques is validated in the

result part. From the analysis of performance, it concludes that fuzzy has the highest accuracy of prediction

over other technologies. Moreover, the each technique have own drawback and advantages. Even the artificial

neural network has the high performance next to fuzzy classifier in the prediction it is slower because of the

learning method. The performance of the K nearest neighbour has reduced when the dataset carries larger

instances.

The need of memory is very large in the support vector machines. The precision value has converted into

descending value while data was reduced in naive Bayes. However, according to the analysis of performance

based on the five parameters, it is concluded that fuzzy shows better classification performance for the

predictionof chronic kidney disease.



5. Conclusion

This paper concentrates on the analysis of classification methodologies for the accurate prediction of chronic

kidney disease. However many techniques are ascended to meet the challenges of classification, it is a

challenging issue in the medical field. In this work, six classification methodologies are weighed with five

parametric comparisons in MATLAB simulation tool. The techniques used in the analysis are an artificial

neural network, K-nearest neighbour, decision tree, naive Bayes, support vector machine and fuzzy. Each

technique has verified with medical dataset having 400 instances. From the evaluation of parameters, the fuzzy

has discovered as the best technique for the classification of chronic kidney disease. Finally, in the result part,

the techniques are discussed for the drawbacks associated with them. As this literature concludes with a future

extension that based on the need of work the better performance classification methodology is expanded and

this is based on the modification and enhancement of the technique with required accuracy of prediction.

Acknowledgements

I would like to thank my co-author Mr. M.K.Jeyakumar for guiding me to complete this research and also am

very thankful to my institution „Noorul Islam Center for Higher Education, Kanyakumari, Tamil Nadu, India‟

for giving me full support to complete this work

Conflict of interest:

R. Subhashini, M.K.Jeyakumar, A state that there are no conflicts of interest. Patients‟ rights and animal

protection statements: This research article does not contain any studies with human or animal subjects.

Ethical statements

Animal and human subjects were not used in this study.

Funding

This Research did not receive any specific grant from funding agencies in the public, commercial or not-for-

profit sectors.

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