Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study

AlMansour, Njoud; Syed, Hajra F.; Khayat, Nuha Radwan; Al-Theeb, Rawan; Juri, Renad Emad; Alhiyafi, Jamal; Alrashed, Saleh; Olatunji, Sunday O.

doi:10.1016/j.compbiomed.2019.04.017

Cited by 222 publications

(115 citation statements)

References 19 publications

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“…With this in mind, several studies has been conducted to increase the sensitivity and specificity by proposing better classification characterized algorithms to reduce the misclassification. Some of them are successfully producing higher sensitivity and specificity algorithms, especially algorithms that related to ASD classification and other medical fields [11][12][13][14][15]. Previous studies conducted by Komisicky et al [11], autistic detection using SVM, Naïve Bayes, decision tree variants, RF.…”

Section: Introductionmentioning

confidence: 99%

“…In other medical fields, in the study [14] which classified chronic kidney disease using SVM and ANN. The higher levels of sensitivity are generated by ANN than SVM.…”

Section: Introductionmentioning

confidence: 99%

“…However, these previous studies did not yet apply their algorithms on ASD datasets in this study. So, this study will use SVM, RF, and ANN to acquire a higher sensitivity and specificity algorithms using the same algorithms as the [11][12][13][14][15] . The aim of this study is to increase the level of sensitivity and specificity in previous studies [10] for ASD classification on 3 AQ-Screening (AQ-Child, AQ-Adolescent, and AQ-Adult).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning algorithm for improving performance on 3 AQ-screening classification

Pratama

Hartanto

Setiawan

2019

CST

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Autism Spectrum Disorder (ASD) classification using machine learning can help parents, caregivers, psychiatrists, and patients to obtain the results of early detection of ASD. In this study, the dataset used is the autism-spectrum quotient for child, adolescent and adult, namely AQ-child, AQ-adolescent, AQ-adult. This study aims to improve the sensitivity and specificity of previous studies so that the classification results of ASD are better characterized by the reduced misclassification. The algorithm applied in this study: support vector machine (SVM), random forest (RF), artificial neural network (ANN). The evaluation results using 10-fold cross validation showed that RF succeeded in producing higher adult AQ sensitivity, which was 87.89%. The increase in the specificity level of AQ-Adolescents is better produced using an SVM of 86.33%.

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Section: Introductionmentioning

confidence: 99%

“…In other medical fields, in the study [14] which classified chronic kidney disease using SVM and ANN. The higher levels of sensitivity are generated by ANN than SVM.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning algorithm for improving performance on 3 AQ-screening classification

Pratama

Hartanto

Setiawan

2019

CST

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“…Besides, Almansour et al compared the ANN and SVM techniques in a dataset of 400 patients to predict CKD in early stage. The empirical results from the experiments indicated that ANN performed better than SVM, with accuracies of 99.75% and 97.75%, respectively [26]. Chen Z et al also used multivariate models, i.e., K-nearest neighbor (KNN), SVM, and soft independent modeling of class analogy (SIMCA), to evaluate risk of 386 patients to predict CKD.…”

Section: Alerting Akimentioning

confidence: 99%

Role of Artificial Intelligence in Kidney Disease

Yuan¹,

Zhang²,

Deng³

et al. 2020

Int. J. Med. Sci.

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Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.

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“…However, a complete process of filling in the missing values is not described in detail, and no feature selection technology is used to select predictors as well. Almansour et al used SVM and neural network to diagnose CKD, and the accuracy of the models was 97.75% and 99.75%, respectively [30]. In the models established by Gunarathne et al, zero was used to fill out the missing values and decision forest achieved the best performance with the accuracy was 99.1% [31].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Methodology for Diagnosing Chronic Kidney Disease

et al. 2020

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Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. Since there are no obvious symptoms during the early stages of CKD, patients often fail to notice the disease. Early detection of CKD enables patients to receive timely treatment to ameliorate the progression of this disease. Machine learning models can effectively aid clinicians achieve this goal due to their fast and accurate recognition performance. In this study, we propose a machine learning methodology for diagnosing CKD. The CKD data set was obtained from the University of California Irvine (UCI) machine learning repository, which has a large number of missing values. KNN imputation was used to fill in the missing values, which selects several complete samples with the most similar measurements to process the missing data for each incomplete sample. Missing values are usually seen in real-life medical situations because patients may miss some measurements for various reasons. After effectively filling out the incomplete data set, six machine learning algorithms (logistic regression, random forest, support vector machine, k-nearest neighbor, naive Bayes classifier and feed forward neural network) were used to establish models. Among these machine learning models, random forest achieved the best performance with 99.75% diagnosis accuracy. By analyzing the misjudgments generated by the established models, we proposed an integrated model that combines logistic regression and random forest by using perceptron, which could achieve an average accuracy of 99.83% after ten times of simulation. Hence, we speculated that this methodology could be applicable to more complicated clinical data for disease diagnosis. INDEX TERMS Chronic kidney disease, machine learning, KNN imputation, integrated model.

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Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study

Cited by 222 publications

References 19 publications

Machine learning algorithm for improving performance on 3 AQ-screening classification

Machine learning algorithm for improving performance on 3 AQ-screening classification

Role of Artificial Intelligence in Kidney Disease

A Machine Learning Methodology for Diagnosing Chronic Kidney Disease

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