Performance Evaluation on Machine Learning Classification Techniques for Disease Classification and Forecasting through Data Analytics for Chronic Kidney Disease (CKD)

2017 ◽  
Author(s):  
W.H.S.D Gunarathne ◽  
K.D.M Perera ◽  
K.A.D.C.P Kahandawaarachchi
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Performance Evaluation ◽  
Kidney Disease ◽  
Data Analytics ◽  
Disease Classification ◽  
Classification Techniques ◽  
Machine Learning Classification

Chronic Kidney Disease Prediction Using Different Algorithms

2020 ◽  
pp. 06-13
Author(s):  
Harsh Vardhan Singh
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Mean Squared Error ◽  
Classification Algorithm ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Squared Error ◽  
Health Damage ◽  
Specific Symptoms

Chronic Kidney Disease (CKD) is a disease which doesn't shows symptoms at all or in some cases it doesn't show any disease specific symptoms it is hard to predict, detect and prevent such a disease and this could be lead to permanently health damage, but machine learning can be hope in this problem it is best in prediction and analysis. The objective of paper is to build the model for predicting the Chronic Kidney Disease using various machine learning classification algorithm. Classification is a powerful machine learning technique that is commonly used for prediction. Some of the classification algorithm are Logistic Regression, Support Vector Machine, Naïve Bayes, Random Forest Classifier, KNN. This paper investigate which algorithm is used for the improving the accuracy in the prediction of Chronic Kidney Disease. And, a comparative analysis on the accuracy and mean squared error is to done for predicting the best model.

scholarly journals Chronic Kidney Disease Diagnosis using Decision Tree Algorithms

2020 ◽  
Author(s):  
Hamida Ilyas ◽  
Sajid Ali ◽  
Mahvish Ponum ◽  
Osman Hasan ◽  
Muhammad Tahir Mahmood
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Random Forest ◽  
Kidney Disease ◽  
Early Detection ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Automated System ◽  
Disease Epidemiology ◽  
Machine Learning Classification

Abstract Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages i.e., early stage to the last stage of kidney failure. Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. In particular, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with a 85.5% accuracy. The study also showed that J48 shows improved performance over Random Forest, so, it may be used to build an automated system for the detection of severity of CKD.

scholarly journals Detecting Kidney Disease using Naïve Bayes and Decision Tree in Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 498-501
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Naive Bayes ◽  
Kidney Diseases ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Machine Learning Classification ◽  
The Relationship

Chronic Kidney Disease (CKD) mostly influence patients suffered from difficulties due to diabetes or high blood pressure and make them unable to carry out their daily activities. In a survey , it has been revealed that one in 12 persons living in two biggest cities of India diagnosed of CKD features that put them at high risk for unfavourable outcomes. In this article, we have analyzed as well as anticipated chronic kidney disease by discovering the hidden pattern of the relationship using feature selection and Machine Learning classification approach like naive Bayes classifier and decision tree(J48). The dataset on which these approaches are applied is taken from UC Irvine repository. Based on certain feature, the approaches will predict whether a person is diagnosed with a CKD or Not CKD. While performing comparative analysis, it has been observed that J48 decision tree gives high accuracy rate in prediction. J48 classifier proves to be efficient and more effective in detecting kidney diseases.

Comparison of Performance of Various Machine Learning Classification Techniques With Ensemble Classifiers for Prediction of Chronic Kidney Disease

2021 ◽  
pp. 245-270
Author(s):  
Noopur Goel
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Ensemble Classifiers ◽  
Machine Learning Classification ◽  
The World ◽  
The Individual ◽  
Better Than

Chronic kidney disease has become a very prevalent problem worldwide and almost 10% of the population is suffering and millions of people are dying every year because of chronic kidney disease. Numerous machine learning and data mining techniques are applied by many researchers round the world to diagnose the presence of chronic kidney disease, so that the patients of chronic kidney disease may get benefitted in terms of getting proper healthcare follow-up. In this chapter, Experiment 1 is conducted by implementing five different classifiers on the original chronic kidney disease dataset. In Experiment 2, two different ensemble classifiers are implemented combining all five individual classifiers. The Results of both the Experiments 1 and 2 are compared, and it is observed that the accuracy of ensemble classifiers is far better than the accuracy of individual classifiers. It may be concluded that the two experiments conducted in the chapter show the performance of ensemble classifiers is better than the individual classifiers.

scholarly journals Chronic kidney disease diagnosis using decision tree algorithms

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hamida Ilyas ◽  
Sajid Ali ◽  
Mahvish Ponum ◽  
Osman Hasan ◽  
Muhammad Tahir Mahmood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Random Forest ◽  
Kidney Disease ◽  
Early Detection ◽  
Disease Diagnosis ◽  
Automated System ◽  
Disease Epidemiology ◽  
Machine Learning Classification ◽  
Improved Performance

Abstract Background Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages. Methods Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning methods are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. Specifically, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Results Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with an accuracy of 85.5%. The study also showed that J48 shows improved performance over Random Forest. Conclusions The study concluded that it may be used to build an automated system for the detection of severity of CKD.

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