Chronic kidney disease prediction using machine learning techniques

2021 ◽  
Author(s):  
G Nandhini ◽  
J Aravinth
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Learning Techniques

scholarly journals Computer-Aided Diagnosis of Chronic Kidney Disease in Developing Countries: A Comparative Analysis of Machine Learning Techniques

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 25407-25419 ◽  
Author(s):  
Alvaro Sobrinho ◽  
Andressa C. M. Da S. Queiroz ◽  
Leandro Dias Da Silva ◽  
Evandro De Barros Costa ◽  
Maria Eliete Pinheiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Developing Countries ◽  
Comparative Analysis ◽  
Kidney Disease ◽  
Computer Aided Diagnosis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals An Empirical Evaluation of Machine Learning Techniques for Chronic Kidney Disease Prophecy

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 55012-55022 ◽  
Author(s):  
Bilal Khan ◽  
Rashid Naseem ◽  
Fazal Muhammad ◽  
Ghulam Abbas ◽  
Sunghwan Kim
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Empirical Evaluation ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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