A HYBRID METHOD FOR DETECTION OF KIDNEY DISEASE USING MACHINE LEARNING
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Abstract
The decline of kidney function is known as Chronic Kidney Disease (CKD). Known also as chronic renal disease, chronic kidney disease is an abnormal functioning of the kidneys or a progressive loss of renal function that develops over months or years. is a significant burden on the healthcare system because to the rising patient population, poor prognosis for morbidity and death, and increased risk of developing end-stage renal disease. yearly because of the illness. Therefore, it takes time, the illness's symptoms are not immediately apparent, and a large number of lives are lost before kidney disease is taken seriously in its early stages. Modern Machine Learning (ML) approaches are being used to detect several key health hazards, including the prediction of diabetes, the detection of brain tumors, the identification of COVID-19, the detection of renal illnesses, and many more. Therefore, the illness may be predicted in this study utilizing these machine learning classifiers, hybrid namely KNN and Logistic Regression. Our major goal is to distinguish between different machine learning algorithms based on their correctness in terms of performance. This hybrid method can produce accurate and dependable F1- score and accuracy outcomes.
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References
S. Vijaiyanti and s. Dayanand, "Kidney Disease Prediction Using SVM and ANN Algorithms", vol. 6, no. 2,
March 2015, ISSN 22296166.
Mahfuzah Mustafa, Mohd Nasir Tab et al., "Comparison between KNN and ANN Classification in Brain
Balancing Application via Spectrogram Image", Journal of Computer Science & Computational Mathematics,
vol. 2, no. 4, pp. 17-22, April 2012.
K Ross, K Leung, Ying Wang et al., "Using a multi-staged strategy based on machine learning and
mathematical modelling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective
case-control cohort analysis", BMC-Nephrology, pp. 1-9, 2013.
ZarPhyu T, Oo NN. Performance comparison of feature selection methods. MATEC Web Conf. 2016;42:2–5.
Rady EA, Anwar AS. Informatics in Medicine Unlocked Prediction of kidney disease stages using data mining
algorithms. Informatics Med. 2019;15(2018):100178.
Salekin A, Stankovic J. Detection of Chronic Kidney Disease and Selecting Important Predictive Attributes.
In: Proc. - 2016 IEEE Int. Conf. Healthc. Informatics, ICHI 2016, pp. 262–270, 2016.
S. Revathy, B. Bharathi, P. Jeyanthi, M. Ramesh, “Chronic Kidney Disease Prediction using Machine Learning
Models”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9
Issue-1, October 2019
Devika R, Sai Vaishnavi Avilala, V. Subramaniyaswamy, “Comparative Study of Classifier for Chronic
Kidney Disease prediction using Naive Bayes, KNN and Random Forest”, Proceedings of the Third International
Conference on Computing Methodologies and Communication (ICCMC 2019), IEEE Xplore Part Number:
CFP19K25-ART; ISBN: 978-1-5386-7808-4
V Ganapathi Raju, K Prasanna Lakshmi, K. Gayathri Praharshitha, Chittampalli Likhitha, “Prediction of
chronic kidney disease (CKD) using Data Science”, Proceedings of the International Conference on Intelligent
Computing and Control Systems (ICICCS 2019), IEEE Xplore Part Number: CFP19K34-ART; ISBN: 978-1-
-8113-8.
Zixian Wang, Jae Won Chung, Xilin Jiang, Yantong Cui, Muning Wang, Anqi Zheng, “ Machine Learning
based prediction system for Chronic Kidney disease Using Associative Classification Techniques”, International
Journal of Engineering and Technology, 7 (4.36) (2018) 1161-1167
David Y. Gaitonde, David L. Cook, Md; And Ian M. Rivera, “Chronic Kidney Disease: Detection and
Evaluation”, 2017 American Academy of family physicians, doi: afp/2017/1215/p776-s1. html
Asif Salekin, John Stankovic, "Detection of Chronic Kidney Disease and Selectiing Important Predictive
Attributes," Proc. IEEE International Conference on Healthcare Informatics (ICHI), IEEE, Oct. 2016,
doi:10.1109/ICHI.2016.36.
Sharma, S., Sharma, V., Sharma, A. (2016). Performance based evaluation of various machine learning
classification techniques for chronic kidney disease diagnosis. arXiv preprint arXiv:1606.09581.
Sahil Sharma, Vinod Sharma, Atul Sharma, “Performance Based Evaluation of Various Machine Learning
Classification Techniques for Chronic Kidney Disease Diagnosis,” July18, 2016.
Radha, N., Ramya, S. (2015). Performance analysis of machine learning algorithms for predicting chronic
kidney disease. Int. J. Comput. Sci. Eng. Open Access, 3, 72-76.