Rule Induction and Prediction of Chronic Kidney Disease Using Boosting Classifiers, Ant-Miner and J48 Decision Tree

2019 ◽  
Author(s):  
Arif-Ul-Islam ◽  
Shamim H Ripon
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Rule Induction ◽  
J48 Decision Tree

A novel enhanced decision tree model for detecting chronic kidney disease

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Avijit Kumar Chaudhuri ◽  
Deepankar Sinha ◽  
Dilip K. Banerjee ◽  
Anirban Das
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Tree Model

Machine-learning algorithm as a prognostic tool in non-obstructive acute-on-chronic kidney disease in the cat

2021 ◽  
pp. 1098612X2110012
Author(s):  
Jade Renard ◽  
Mathieu R Faucher ◽  
Anaïs Combes ◽  
Didier Concordet ◽  
Brice S Reynolds
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Diagnostic Performance ◽  
Clinical Signs ◽  
Learning System ◽  
Clinical Severity ◽  
Medium Term ◽  
Term Survival

Objectives The aim of this study was to develop an algorithm capable of predicting short- and medium-term survival in cases of intrinsic acute-on-chronic kidney disease (ACKD) in cats. Methods The medical record database was searched to identify cats hospitalised for acute clinical signs and azotaemia of at least 48 h duration and diagnosed to have underlying chronic kidney disease based on ultrasonographic renal abnormalities or previously documented azotaemia. Cases with postrenal azotaemia, exposure to nephrotoxicants, feline infectious peritonitis or neoplasia were excluded. Clinical variables were combined in a clinical severity score (CSS). Clinicopathological and ultrasonographic variables were also collected. The following variables were tested as inputs in a machine learning system: age, body weight (BW), CSS, identification of small kidneys or nephroliths by ultrasonography, serum creatinine at 48 h (Crea48), spontaneous feeding at 48 h (SpF48) and aetiology. Outputs were outcomes at 7, 30, 90 and 180 days. The machine-learning system was trained to develop decision tree algorithms capable of predicting outputs from inputs. Finally, the diagnostic performance of the algorithms was calculated. Results Crea48 was the best predictor of survival at 7 days (threshold 1043 µmol/l, sensitivity 0.96, specificity 0.53), 30 days (threshold 566 µmol/l, sensitivity 0.70, specificity 0.89) and 90 days (threshold 566 µmol/l, sensitivity 0.76, specificity 0.80), with fewer cats still alive when their Crea48 was above these thresholds. A short decision tree, including age and Crea48, predicted the 180-day outcome best. When Crea48 was excluded from the analysis, the generated decision trees included CSS, age, BW, SpF48 and identification of small kidneys with an overall diagnostic performance similar to that using Crea48. Conclusions and relevance Crea48 helps predict short- and medium-term survival in cats with ACKD. Secondary variables that helped predict outcomes were age, CSS, BW, SpF48 and identification of small kidneys.

scholarly journals Machine Learning Framework to Predict Chronic Kidney Disease using Ensemble Algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 1-6
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Random Forest ◽  
Kidney Disease ◽  
Decision Tree ◽  
Performance Metrics ◽  
Weighted Average ◽  
Gradient Boosting ◽  
Support Vector ◽  
The Individual

Chronic Kidney Disease (CKD) is a worldwide concern that influences roughly 10% of the grown-up population on the world. For most of the people the early diagnosis of CKD is often not possible. Therefore, the utilization of present-day Computer aided supported strategies is important to help the conventional CKD finding framework to be progressively effective and precise. In this project, six modern machine learning techniques namely Multilayer Perceptron Neural Network, Support Vector Machine, Naïve Bayes, K-Nearest Neighbor, Decision Tree, Logistic regression were used and then to enhance the performance of the model Ensemble Algorithms such as ADABoost, Gradient Boosting, Random Forest, Majority Voting, Bagging and Weighted Average were used on the Chronic Kidney Disease dataset from the UCI Repository. The model was tuned finely to get the best hyper parameters to train the model. The performance metrics used to evaluate the model was measured using Accuracy, Precision, Recall, F1-score, Mathew`s Correlation Coefficient and ROC-AUC curve. The experiment was first performed on the individual classifiers and then on the Ensemble classifiers. The ensemble classifier like Random Forest and ADABoost performed better with 100% Accuracy, Precision and Recall when compared to the individual classifiers with 99.16% accuracy, 98.8% Precision and 100% Recall obtained from Decision Tree Algorithm

scholarly journals Chronic Kidney Disease Prediction by Using Different Decision Tree Techniques

2019 ◽  
Vol 1255 ◽  
pp. 012024
Author(s):  
I.A. Pasadana ◽  
D. Hartama ◽  
M. Zarlis ◽  
A.S. Sianipar ◽  
A. Munandar ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Disease Prediction

scholarly journals P1835PREDICTION OF CHRONIC OF KIDNEY FAILURE BASED ON ARTIFICIAL INTELLIGENCE SYSTEM USING THE FUZZY DECISION TREE ALGORITHM

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Rifaldy Fajar ◽  
Prihantini Jupri
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Kidney Failure ◽  
Laboratory Tests ◽  
Chronic Kidney Failure ◽  
Decision Tree Algorithm ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Tree Algorithm

Abstract Background and Aims Chronic kidney disease is one type of disease that can cause death. Until now, chronic kidney failure has no antidote, so this disease cannot be cured but can be slowed down or stopped its development. Early diagnosis of this disease will help to prevent these fatal consequences. To diagnose this disease, several laboratory tests are needed in which the results of these tests will be calculated and concluded the results by a doctor or medical practitioner. The development of science and technology, especially in the field of computers will help the work of doctors to analyze the results of laboratory tests become easier and faster. In this study, a prediction attempt is made using the Fuzzy Decision Tree classification algorithm, which is expected to obtain high accuracy results. Method This study uses the Chronic Kidney Disease (CKD) dataset taken from the UCI Machine Learning Repository. Data was collected from the hospital for approximately two months. This dataset covers a total of 400 samples with numerical attributes totaling 11 columns and nominal totaling 14 columns. Data samples were provided as many as 400 rows with 250 samples being the ckd group (positive for chronic kidney failure) and 150 samples for the notckd group (chronic kidney failure). But after going through the preprocessing stage, data that can be used amounted to 158 rows with 43 samples are the ckd group (positive chronic kidney failure) and 115 samples of the notckd group (negative chronic kidney failure). Results The trial was conducted using several predetermined thresholds and the most optimal accuracy was 98.3%, which showed a fairly high degree of accuracy. Conclusion Thus, it can be concluded that the Fuzzy Decision Tree algorithm can be said to be able to predict chronic kidney failure with a very good results.

scholarly journals An Intelligent Web-Based Decision Support System for Monitoring the Chronic Kidney Disease in Brazilian Communities

2020 ◽  
Author(s):  
Álvaro Sobrinho ◽  
Andressa C. M. da S. Queiroz ◽  
Gyovanne Bezerra Cavalcanti ◽  
Josaias de Moura Silva ◽  
Leandro Dias da Silva ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Developing Countries ◽  
Decision Support ◽  
Kidney Disease ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Kappa Statistic ◽  
Web Based ◽  
Knowledge Based

Abstract Background: Chronic Kidney Disease (CKD) is a worldwide health problem, usually diagnosed in late stages of the disease, increasing public health costs and mortality rates. The late diagnosis is even more critical in developing countries due to the high levels of poverty, a large number of hard-to-reach locations, and sometimes lack/precarious primary care.Methods: We designed and evaluated an intelligent web-based Decision Support System (DSS) using the J48 decision tree machine learning algorithm, knowledge-based system concepts, the clinical document architecture, Cohen's kappa statistic, and interviews with an experienced nephrologist.Results: We provided a DSS methodology, that guided the development of the system to assist patients, primary care physicians, and the government in identifying and monitoring the CKD in Brazilian communities. The system provides remote monitoring features. A CKD dataset enabled the evaluation of the J48 decision tree algorithm, while Cohen's kappa statistic guided the evaluation of the knowledge-based system by interviews with an experienced nephrologist. Conclusion: The DSS facilitates the identification and monitoring of the CKD considering low-income populations in Brazil. In addition, the methodology and DSS can be re-used in other developing countries with similar scenarios. Trial registration: 47350313.9.0000.5013.

scholarly journals An Intelligent Web-Based Decision Support System for Monitoring the Chronic Kidney Disease in Brazilian Communities

2020 ◽  
Author(s):  
Álvaro Sobrinho ◽  
Andressa C. M. da S. Queiroz ◽  
Gyovanne Bezerra Cavalcanti ◽  
Josaias de Moura Silva ◽  
Leandro Dias da Silva ◽  
...  
Keyword(s):  
Public Health ◽  
Primary Care ◽  
Chronic Kidney Disease ◽  
Developing Countries ◽  
Decision Support ◽  
Kidney Disease ◽  
Decision Tree ◽  
Kappa Statistic ◽  
Web Based ◽  
Knowledge Based

Abstract Background: Chronic Kidney Disease (CKD) is a worldwide public health problem, usually diagnosed in the late stages of the disease, increasing public health costs and mortality rates. The late diagnosis is even more critical in developing countries due to the high levels of poverty, a large number of hard-to-reach locations, and sometimes lack/precarious primary care. Methods: We designed and evaluated an intelligent web-based Decision Support System (DSS) using the J48 decision tree machine learning algorithm, knowledge-based system concepts, the clinical document architecture, Cohen's kappa statistic, and interviews with an experienced nephrologist. Results: We provided a DSS methodology that guided the development of the system, that provides remote monitoring features, to assist patients, primary care physicians, and the government in identifying and monitoring the CKD in Brazilian communities. A CKD dataset enabled the training and evaluation of the J48 decision tree algorithm, while Cohen's kappa statistic guided the evaluation of the knowledge-based system by interviews with an experienced nephrologist. Conclusion: The DSS facilitates the identification and monitoring of the CKD considering low-income populations in Brazil. In addition, the methodology and DSS can be reused in other developing countries with similar scenarios.

scholarly journals Comparison of Machine Learning With Logistic Regression for Prediction of Chronic Kidney Disease in the Thai Adult Population

2021 ◽  
Vol 44 (4) ◽  
pp. 1-12
Author(s):  
Ratchainant Thammasudjarit ◽  
Punnathorn Ingsathit ◽  
Sigit Ari Saputro ◽  
Atiporn Ingsathit ◽  
Ammarin Thakkinstian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Logistic Regression ◽  
Random Forest ◽  
Kidney Disease ◽  
Decision Tree ◽  
Prediction Model ◽  
Prediction Models ◽  
The Neural Network

Background: Chronic kidney disease (CKD) takes huge amounts of resources for treatments. Early detection of patients by risk prediction model should be useful in identifying risk patients and providing early treatments. Objective: To compare the performance of traditional logistic regression with machine learning (ML) in predicting the risk of CKD in Thai population. Methods: This study used Thai Screening and Early Evaluation of Kidney Disease (SEEK) data. Seventeen features were firstly considered in constructing prediction models using logistic regression and 4 MLs (Random Forest, Naïve Bayes, Decision Tree, and Neural Network). Data were split into train and test data with a ratio of 70:30. Performances of the model were assessed by estimating recall, C statistics, accuracy, F1, and precision. Results: Seven out of 17 features were included in the prediction models. A logistic regression model could well discriminate CKD from non-CKD patients with the C statistics of 0.79 and 0.78 in the train and test data. The Neural Network performed best among ML followed by a Random Forest, Naïve Bayes, and a Decision Tree with the corresponding C statistics of 0.82, 0.80, 0.78, and 0.77 in training data set. Performance of these corresponding models in testing data decreased about 5%, 3%, 1%, and 2% relative to the logistic model by 2%. Conclusions: Risk prediction model of CKD constructed by the logit equation may yield better discrimination and lower tendency to get overfitting relative to ML models including the Neural Network and Random Forest.  

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.

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