Author(s): Pardeep Kumar Sharma, Amit Sachdeva, Cherry Bhargava

Email(s): pardeepdeep1@gmail.com , Pardeep.kumar1@lpu.co.in

DOI: 10.52711/0974-360X.2021.00457   

Address: Pardeep Kumar Sharma1*, Amit Sachdeva2, Cherry Bhargava3
1School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, India.
2,3School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, India.
*Corresponding Author

Published In:   Volume - 14,      Issue - 5,     Year - 2021


ABSTRACT:
Clinical judgements can be improved by the use of artificial intelligence (AI) in the routine examinations. In case of chronic kidney diseases (CKD), it is quite difficult to detect at the early stages and afterwards the patient’s condition worsens very quickly. This is only because of the non-prominent disease specific symptoms at the early stages. An early prediction of AKI and CKD with machine learning can be a key to diagnose and reduces the cost of treatment. By using medical data mining of renal patients an intelligent decision support system (DSS) is designed using MATLAB environment, which enables the user to predict the various condition with maximum accuracy of prediction; whether the disease occurs or not and if yes then what is its severity.


Cite this article:
Pardeep Kumar Sharma, Amit Sachdeva, Cherry Bhargava. Fuzzy logic: A tool to predict the Renal diseases. Research Journal of Pharmacy and Technology. 2021; 14(5):2598-2. doi: 10.52711/0974-360X.2021.00457

Cite(Electronic):
Pardeep Kumar Sharma, Amit Sachdeva, Cherry Bhargava. Fuzzy logic: A tool to predict the Renal diseases. Research Journal of Pharmacy and Technology. 2021; 14(5):2598-2. doi: 10.52711/0974-360X.2021.00457   Available on: https://rjptonline.org/AbstractView.aspx?PID=2021-14-5-41


REFERENCES:
1.    Webster, A.C., et al., Chronic kidney disease. The Lancet, 2017. 389(10075): p. 1238-1252.
2.    Nahas, M.E., The global challenge of chronic kidney disease. Kidney International, 2005. 68(6): p. 2918-2929.
3.    Coresh, J., et al., Prevalence of chronic kidney disease in the United States. JAMA, 2007. 298(17): p. 2038-2047.
4.    Alebiosu, C. and O. Ayodele, The global burden of chronic kidney disease and the way forward. Ethnicity and Disease, 2005. 15(3): p. 418.
5.    Mahdavi-Mazdeh, M., Why do we need chronic kidney disease screening and which way to go? Iranian Journal of Kidney Diseases, 2010. 4(4): p. 275.
6.    Khalkhaali, H., et al., Prediction of kidney failure in patients with chronic renal transplant dysfunction. Iranian Journal of Epidemiology, 2010. 6(2): p. 25-31.
7.    Kasper, D., et al., Harrison's principles of internal medicine. 2018: McGraw-Hill Professional Publishing.
8.    Moe, S., et al., Definition, evaluation, and classification of renal osteodystrophy: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney International, 2006. 69(11): p. 1945-1953.
9.    Shinde, S.A. and P.R. Rajeswari, Intelligent health risk prediction systems using machine learning: a review. International Journal of Engineering & Technology 2018. 7(3): p. 1019-1023.
10.    Norouzi, J., et al., Predicting renal failure progression in chronic kidney disease using integrated intelligent fuzzy expert system. Computational and Mathematical Methods in Medicine, 2016. 2016.
11.    Tomašev, N., et al., A clinically applicable approach to continuous prediction of future acute kidney injury. Nature, 2019. 572(7767): p. 116.
12.    Xiao, J., et al., Comparison and development of machine learning tools in the prediction of chronic kidney disease progression. Journal of Translational Medicine, 2019. 17(1): p. 119.
13.    Gunarathne, W., K. Perera, and K. Kahandawaarachchi. Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for Chronic Kidney Disease (CKD). in 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE). 2017. IEEE.
14.    Ramya, S. and N. Radha, Diagnosis of chronic kidney disease using machine learning algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 2016. 4(1): p. 812-820.
15.    Arasu, S.D. and R. Thirumalaiselvi, Review of Chronic Kidney Disease based on Data Mining Techniques. International Journal of Applied Engineering Research, 2017. 12(23): p. 13498-13505.
16.    Salekin, A. and J. Stankovic. Detection of chronic kidney disease and selecting important predictive attributes. in 2016 IEEE International Conference on Healthcare Informatics (ICHI). 2016. IEEE.
17.    Yildirim, P. Chronic kidney disease prediction on imbalanced data by multilayer perceptron: Chronic kidney disease prediction. in 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC). 2017. IEEE.
18.    Sharma, S., V. Sharma, and A. Sharma, Performance based evaluation of various machine learning classification techniques for chronic kidney disease diagnosis. Ar Xiv preprint arXiv:1606.09581, 2016.
19.    Wang, W., J. Yang, and S.Y. Philip, Efficient mining of weighted association rules (WAR). 2000: IBM Thomas J. Watson Research Division.
20.    Liu, B., W. Hsu, and Y. Ma. Integrating classification and association rule mining. in KDD. 1998.
21.    Stensmo, M. and T.J. Sejnowski. Automated medical diagnosis based on decision theory and learning from cases. in World Congress on Neural Networks. 1996.
22.    Quinlan, J.R., C4. 5: Programs for Machine Learning. 2014: Elsevier.

Recomonded Articles:

Research Journal of Pharmacy and Technology (RJPT) is an international, peer-reviewed, multidisciplinary journal.... Read more >>>

RNI: CHHENG00387/33/1/2008-TC                     
DOI: 10.5958/0974-360X 

1.3
2021CiteScore
 
56th percentile
Powered by  Scopus


SCImago Journal & Country Rank

Journal Policies & Information


Recent Articles




Tags


Not Available