Fuzzy logic: A tool to predict the Renal diseases

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.

[1]  M. Nahas The global challenge of chronic kidney disease. , 2005 .

[2]  Sibo Zhu,et al.  Comparison and development of machine learning tools in the prediction of chronic kidney disease progression , 2019, Journal of Translational Medicine.

[3]  Mitra Mahdavi-Mazdeh,et al.  Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System , 2016, Comput. Math. Methods Medicine.

[4]  Suman V. Ravuri,et al.  A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury , 2019, Nature.

[5]  John A. Stankovic,et al.  Detection of Chronic Kidney Disease and Selecting Important Predictive Attributes , 2016, 2016 IEEE International Conference on Healthcare Informatics (ICHI).

[6]  K. A. D. C. P. Kahandawaarachchi,et al.  Performance Evaluation on Machine Learning Classification Techniques for Disease Classification and Forecasting through Data Analytics for Chronic Kidney Disease (CKD) , 2017, 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE).

[7]  M. Mahdavi-Mazdeh Why do we need chronic kidney disease screening and which way to go? , 2010, Iranian journal of kidney diseases.

[8]  Santosh A. Shinde,et al.  Intelligent health risk prediction systems using machine learning: a review , 2018 .

[9]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[10]  O. Ayodele,et al.  The global burden of chronic kidney disease and the way forward. , 2005, Ethnicity & disease.

[11]  J. Coresh,et al.  Prevalence of chronic kidney disease in the United States. , 2007, JAMA.

[12]  G. Eknoyan,et al.  Definition, evaluation, and classification of renal osteodystrophy: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). , 2006, Kidney international.

[13]  Philip S. Yu,et al.  Efficient mining of weighted association rules (WAR) , 2000, KDD '00.

[14]  Pinar Yildirim,et al.  Chronic Kidney Disease Prediction on Imbalanced Data by Multilayer Perceptron: Chronic Kidney Disease Prediction , 2017, 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC).