Data Analysis of Impaired Renal and Cardiac Function Using a Combination of Standard Classifiers
| العنوان: | Data Analysis of Impaired Renal and Cardiac Function Using a Combination of Standard Classifiers |
|---|---|
| المؤلفون: | Danijela Tasic, Drasko Furundzic, Katarina Djordjevic, Slobodanka Galovic, Zorica Dimitrijevic, Sonja Radenkovic |
| المصدر: | Journal of Personalized Medicine Volume 13 Issue 3 Pages: 437 |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | kidney, machine learning, k-nearest neighbor, markers, Medicine (miscellaneous), forecasting ensembles, heart, naive Bayes classifier, neural networks |
| الوصف: | 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 k-nearest neighbors (k-NN) and naive Bayes classifier, decision tree, and a classifier based on logistic regression. The results showed that in MLP, k-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. |
| وصف الملف: | application/pdf |
| تدمد: | 2075-4426 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::033ada69d10186e64aa996ee6702f534 https://doi.org/10.3390/jpm13030437 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....033ada69d10186e64aa996ee6702f534 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20754426 |
|---|