Good

1. By
R.GAYATHRI M.sc., M.Phil.,
Under the guidance of
DR.I.LAURENCE AROQUIARAJ, M.Sc., M.Phil., M.Tech., M.C.A., Ph.D.
Assistant Professor,
Department of computer science,
Periyar University,
Salem-11.
.

2. AGENDA
1
2
LITERATURE SURVEY
EXPERIMENTAL RESULTS
3
4
5
6

3. ABSTRACT
Data mining uses a relatively large amount of
computing power operations on a large set of
data to determine regularities and connections
between data points.
Keywords: Data mining, machine learning, chronic kidney disease

4. INTRODUCTION
 Data Mining brings a set of tools and
techniques that can be applied to this
processed data to discover hidden patterns.
That provides healthcare professionals an
additional source of knowledge for making
decisions.
 The various Data mining classification
approaches and machine learning algorithms
are useful for prediction of chronic diseases.

5. LITERATURE SURVEY
AUTHOR/
YEAR
TITLE METHODOLOGY PROBLEM
STATEMENT
K.R.Lakshmi.Y.Nag
esh,
M.VeeraKrishna.et.a
l
Performance Comparison Of Three Data
Mining Techniques For Predicting
Kidney Dialysis Survivability
Artifical Neural
Network(ANN). Kidney dialysis
Divya Jain et.al A Comparison of Data Mining Tools
using the implementation of C4.5
Algorithm
C4.5 Small set of datas only
used.
P.Swathi Baby, T.
Panduranga Vital
Statistical Analysis and Predicting
Kidney Diseases using Machine
Learning Algorithms
Machine learning algorithms
Statistical analysis and
Predicting kidney diseases
Lambodar Jena,
Narendra Ku.
Kamila.et.al
Distributed Data Mining Classification
Algorithms for Prediction of Chronic-
Kidney-Disease
J48,Naïve Bayes,Random
forest. predict chronic kidney
disease (CKD)
Dr.S.Vijayarani,
Mr.S.Dhayanand.et
“Liver Disease Prediction using SVM
and Naïve Bayes Algorithms
Decision Tree, SVM, Naïve
Bayes. Chronic renal disease
Prediction

6. EXPERIMENTAL RESULTS
Evolution Criteria C4.5 SVM J48 MLP
Time to build
model (s)
0.08 0.41 0.03 8.14
Correctly classified instances 396 391 396 399
Incorrectly classified instances 4 9 4 1
Accuracy (%) 63 60.25 62.5 75
Error 0.37 0.39 0.36 0.25

7. EXPERIMENTAL RESULTS

8. CONCLUSION
 In this work,we used several learning
algorithm C4.5,SVM,J48 and MLP,to predict
patients with chronic kidney failure disease
(ckd),and patients who are not suffering from
this disease (notckd).
 Simulation results showed that MLP classifier
proved its performance in predicting with best
results in terms of accuracy and maximum
execution time.

9. REFERENCES
 Murat koklu, Kemal Tutune “Classification Of Chronic Kidney Disease with
most known data mining methods”, International Conference, Rabat,
Morocco, 8th -9th December 2016, ISBN: 978-93-86291-60-8.
 Naganna Chetty, Kunwar Singh Vaisla, Sithu D Sudarsan, Role of Attributes
Selection in Classification of Chronic Kidney Disease Patients,
http:/ieeexplore.ieee.org/document/7374193/
 Basma Boukenze , Hajar Mousannif and Abdelkrim Haqiq “Performance of
Data Mining Techniques To Predict In Healthcare Case Study: Chronic
Kidney Failure Disease”, International Journal of Database
Management Systems ( IJDMS ) Vol.8, No.3, June 2016.
 Xindong Wu · Vipin Kumar “Top 10 algorithms in data mining”, Received: 9
July 2007 / Revised: 28 September 2007 / Accepted: 8 October 2007
Published online: 4 December 2007 © Springer-Verlag London Limited
2007.
 K.R.Lakshmi, Y.Nagesh and M.VeeraKrishna,“Performance Comparison Of
Three Data Mining Techniques For Predicting Kidney Dialysis Survivability",
International Journal of Advances in Engineering and Technology,Mar-

10.  Divya Jai “A Comparison of Data Mining Tools using the implementation of C4.5
Algorithm”,International Journal Of Science and Research (IJSR),ISSN(online):2319-
7064,Impact Factor(2012):3.358,volume3,Issue 8,Augest 2014.
 P.Swathi Baby, T. Panduranga Vita,”Statistical Analysis and Predicting Kidney
Diseases using Machine Learning Algorithms”,International Journal of Engineering
Research and Technology(IJERT) Vol.4 Issue07,July-2015.
 Lambodar Jena, Narendra Ku. Kamila,”Distributed Data Mining Classification
Algorithms for Prediction of Chronic- Kidney-Disease”, International Journal of
Emerging Research in Management &Technology ISSN: 2278-9359 (Volume-4,
Issue-11).
 Dr. S. Vijayarani, Mr.S.Dhayanand “Liver Disease Prediction using SVM and Naïve
Bayes Algorithms”, International Journal of Science, Engineering and Technology
Research (IJSETR) Volume 4, Issue 4, April 2015.
 Ashfaq Ahmed K, Sultan Aljahdali,” Comparative Prediction Performance with
Support Vector Machine and Random Forest Classification Techniques”,
International Journal of Computer Applications (0975 – 8887) Volume 69– No.11,
May 2013