Risk Prediction of Kidney Disease Using Machine Learning Strategies

Jena, Lambodar; Patra, Bichitrananda; Nayak, Soumen; Mishra, Sushruta; Tripathy, Sushreeta

doi:10.1007/978-981-15-6202-0_50

Cited by 39 publications

(5 citation statements)

References 2 publications

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“…Table 13 presents the comparison of performance between the previous studies and our work on the same dataset. In our work, the Relief-F feature selection methods have achieved the best performance for the testing results and cross-validation results using DT and GBT Classifier compared to the other existing works [ 23 , 24 , 26 , 27 , 30 ]. Also, our work is different from the other existing works [ 22 , 25 ] because it registered the results for both the training set and the testing set, and it has achieved the best performance.…”

Section: Experiments and Resultsmentioning

confidence: 78%

“…The results showed that RF with Random Forest feature selection had achieved the best performance with 98.8% accuracy. In [ 26 ], the genetic search algorithm has been used to select the most important features from the CKD dataset. Decision Table, J48, Multilayer Perceptron (MLP), and NB have been applied to both full features and the selected features.…”

Section: Related Workmentioning

confidence: 99%

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Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark

Abdel-Fattah

Othman

Goher

2022

Computational Intelligence and Neuroscience

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Chronic kidney disease (CKD) has become a widespread disease among people. It is related to various serious risks like cardiovascular disease, heightened risk, and end-stage renal disease, which can be feasibly avoidable by early detection and treatment of people in danger of this disease. The machine learning algorithm is a source of significant assistance for medical scientists to diagnose the disease accurately in its outset stage. Recently, Big Data platforms are integrated with machine learning algorithms to add value to healthcare. Therefore, this paper proposes hybrid machine learning techniques that include feature selection methods and machine learning classification algorithms based on big data platforms (Apache Spark) that were used to detect chronic kidney disease (CKD). The feature selection techniques, namely, Relief-F and chi-squared feature selection method, were applied to select the important features. Six machine learning classification algorithms were used in this research: decision tree (DT), logistic regression (LR), Naive Bayes (NB), Random Forest (RF), support vector machine (SVM), and Gradient-Boosted Trees (GBT Classifier) as ensemble learning algorithms. Four methods of evaluation, namely, accuracy, precision, recall, and F1-measure, were applied to validate the results. For each algorithm, the results of cross-validation and the testing results have been computed based on full features, the features selected by Relief-F, and the features selected by chi-squared feature selection method. The results showed that SVM, DT, and GBT Classifiers with the selected features had achieved the best performance at 100% accuracy. Overall, Relief-F’s selected features are better than full features and the features selected by chi-square.

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Section: Experiments and Resultsmentioning

confidence: 78%

Section: Related Workmentioning

confidence: 99%

Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark

Abdel-Fattah

Othman

Goher

2022

Computational Intelligence and Neuroscience

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“…Jena et al, [12] have categorised recent advancements in imaging technology, including terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). from a summary of biological image analysis.…”

Section: Literature Surveymentioning

confidence: 99%

Efficient classification of kidney disease detection using Heterogeneous Modified Artificial Neural Network and Fruit Fly Optimization Algorithm

J. Deepika,

P. Selvaraju,

Mahesh Kumar Thota

et al. 2023

ARASET

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Chronic kidney disease (CKD), a significant issue for public health, affects millions of individuals globally. The course of end-stage renal disease must be stopped or reversed, hence it is crucial to find chronic kidney disease early in order to receive therapy. Prediction of CKD is a second source of treatment, as machine learning techniques, with their high classification accuracy, are becoming increasingly significant in medical diagnosis. To learn about CKD and associated issues in this situation, deep learning is used. The sole inputs used to construct the three distinct types of models were the retinal fundus image alone (test model), the covariate only (the reference model), and the retinal fundus image plus covariate (hybrid model). To maintain the accuracy of contemporary classification systems, feature selection techniques must be applied correctly in order to reduce data size. Here, recommend the Fruit fly optimisation algorithm (FFOA) and the heterogeneous artificial neural network (HMANN) in this paper for efficient disease categorization. An Internet of Medical Things (IoMT) platform is presented for the early detection, segmentation, and diagnosis of chronic renal failure using a heterogeneous modified artificial neural network (HMANN). The Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms are used to classify the suggested HMANN. The ideal feature is chosen using an FFOA from a large pool of candidate features. The proposed method uses ultrasound pictures as its foundation and, as a first step in processing, slices a region of interest in the kidney in the ultrasound image. The accuracy, sensitivity, specificity, positive predictive power, negative predictive power, false positive rate, and false negative rate of the suggested CKD classification system were all taken into consideration when evaluating its performance.

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“…The key features from the CKD dataset were chosen using the genetic search algorithm in [17]. Both the whole set of features and the chosen features have been subjected to DT [18], the authors employed three base-learners KNN, DT and NB along with two ensemble techniques, Bagging and Random Subspace methods-to predict CKD.…”

Section: Svm and Ann Correlation Coefficientsmentioning

confidence: 99%

Ensemble of Homogenous and Heterogeneous Classifiers using K-Fold Cross Validation with Reduced Entropy

Venkatarathinam,

Sivakami,

Christodoss

et al. 2023

IJRITCC

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Chronic kidney disease (CKD) affects millions of people worldwide, greatly reducing their quality of life and creating serious economic, social, and medical problems. Some automated diagnosis methods can detect chronic renal disease. In-depth studies on data mining techniques have recently focused on accuracy in the diagnosis of chronic renal illnesses, either by taking advantage of the disease's simplicity or doing feature selection in addition to pre-processing. In order to handle the unbalanced dataset in this work, Synthetic Minority Over Sampling Technique (SMOTE) is used during pre-processing. For this investigation, 400 data from the publicly accessible UCI machine learning (ML) repository are used. For the implementation, both homogeneous and heterogeneous ensemble classifiers which combine two separate classifiers have been used. Different machine learning (ML) techniques, such as the Classification and Regression Tree (CART), Adaboost classifier, Decision Tree (DT), Reduced Error Pruning Tree, Alternating Decision Tree, and Random Forests Algorithm and their ensembles with a significant reduction in entropy, are used to perform the classification. With a 99.12% accuracy rate and a 99.10% f1 score, the homogeneous classifier Adaboost-Random Forest outperforms other models in the prediction of CKD.

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Risk Prediction of Kidney Disease Using Machine Learning Strategies

Cited by 39 publications

References 2 publications

Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark

Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark

Efficient classification of kidney disease detection using Heterogeneous Modified Artificial Neural Network and Fruit Fly Optimization Algorithm

Ensemble of Homogenous and Heterogeneous Classifiers using K-Fold Cross Validation with Reduced Entropy

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