Data Analysis of Impaired Renal and Cardiac Function Using a Combination of Standard Classifiers
We examine the significance of the predictive potential of EPI cystatin C (EPI CysC) in combination with NTproBNP, sodium, and potassium in the evaluation of renal function in patients with cardiorenal syndrome using standard mathematical classification models from the domain of artificial intellige...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/13/3/437 |
| _version_ | 1797610790783549440 |
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| author | Danijela Tasic Drasko Furundzic Katarina Djordjevic Slobodanka Galovic Zorica Dimitrijevic Sonja Radenkovic |
| author_facet | Danijela Tasic Drasko Furundzic Katarina Djordjevic Slobodanka Galovic Zorica Dimitrijevic Sonja Radenkovic |
| author_sort | Danijela Tasic |
| collection | DOAJ |
| description | We examine the significance of the predictive potential of EPI cystatin C (EPI CysC) in combination with NTproBNP, sodium, and potassium in the evaluation of renal function in patients with cardiorenal syndrome using standard mathematical classification models from the domain of artificial intelligence. The criterion for the inclusion of subjects with combined impairment of heart and kidney function in the study was the presence of newly discovered or previously diagnosed clinically manifest cardiovascular disease and acute or chronic kidney disease in different stages of evolution. In this paper, five standard classifiers from the field of machine learning were used for the analysis of the obtained data: ensemble of neural networks (MLP), ensemble of <i>k</i>-nearest neighbors (<i>k</i>-NN) and naive Bayes classifier, decision tree, and a classifier based on logistic regression. The results showed that in MLP, <i>k</i>-NN, and naive Bayes, EPI CysC had the highest predictive potential. Thus, our approach with utility classifiers recognizes the essence of the disorder in patients with cardiorenal syndrome and facilitates the planning of further treatment. |
| first_indexed | 2024-03-11T06:19:02Z |
| format | Article |
| id | doaj.art-44399be60cc24c27b5901bd66c61c42f |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-11T06:19:02Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-44399be60cc24c27b5901bd66c61c42f2023-11-17T12:02:19ZengMDPI AGJournal of Personalized Medicine2075-44262023-02-0113343710.3390/jpm13030437Data Analysis of Impaired Renal and Cardiac Function Using a Combination of Standard ClassifiersDanijela Tasic0Drasko Furundzic1Katarina Djordjevic2Slobodanka Galovic3Zorica Dimitrijevic4Sonja Radenkovic5Clinic of Nephrology, UCC Nis, Medical Faculty, University of Nis, 18000 Nis, SerbiaInstitute “Mihajlo Pupin”, University of Belgrade, 11060 Belgrade, SerbiaVinca Institute of Nuclear Science—National Institute of the Republic of Serbia, University of Belgrade, 11000 Belgrade, SerbiaVinca Institute of Nuclear Science—National Institute of the Republic of Serbia, University of Belgrade, 11000 Belgrade, SerbiaClinic of Nephrology, UCC Nis, Medical Faculty, University of Nis, 18000 Nis, SerbiaClinic of Nephrology, UCC Nis, Medical Faculty, University of Nis, 18000 Nis, SerbiaWe examine the significance of the predictive potential of EPI cystatin C (EPI CysC) in combination with NTproBNP, sodium, and potassium in the evaluation of renal function in patients with cardiorenal syndrome using standard mathematical classification models from the domain of artificial intelligence. The criterion for the inclusion of subjects with combined impairment of heart and kidney function in the study was the presence of newly discovered or previously diagnosed clinically manifest cardiovascular disease and acute or chronic kidney disease in different stages of evolution. In this paper, five standard classifiers from the field of machine learning were used for the analysis of the obtained data: ensemble of neural networks (MLP), ensemble of <i>k</i>-nearest neighbors (<i>k</i>-NN) and naive Bayes classifier, decision tree, and a classifier based on logistic regression. The results showed that in MLP, <i>k</i>-NN, and naive Bayes, EPI CysC had the highest predictive potential. Thus, our approach with utility classifiers recognizes the essence of the disorder in patients with cardiorenal syndrome and facilitates the planning of further treatment.https://www.mdpi.com/2075-4426/13/3/437kidneyheartmarkersmachine learningneural networksforecasting ensembles |
| spellingShingle | Danijela Tasic Drasko Furundzic Katarina Djordjevic Slobodanka Galovic Zorica Dimitrijevic Sonja Radenkovic Data Analysis of Impaired Renal and Cardiac Function Using a Combination of Standard Classifiers Journal of Personalized Medicine kidney heart markers machine learning neural networks forecasting ensembles |
| title | Data Analysis of Impaired Renal and Cardiac Function Using a Combination of Standard Classifiers |
| title_full | Data Analysis of Impaired Renal and Cardiac Function Using a Combination of Standard Classifiers |
| title_fullStr | Data Analysis of Impaired Renal and Cardiac Function Using a Combination of Standard Classifiers |
| title_full_unstemmed | Data Analysis of Impaired Renal and Cardiac Function Using a Combination of Standard Classifiers |
| title_short | Data Analysis of Impaired Renal and Cardiac Function Using a Combination of Standard Classifiers |
| title_sort | data analysis of impaired renal and cardiac function using a combination of standard classifiers |
| topic | kidney heart markers machine learning neural networks forecasting ensembles |
| url | https://www.mdpi.com/2075-4426/13/3/437 |
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