Prediction of chronic kidney disease using urinary dielectric properties and support vector machine
In this study, we aim to classify the urinary dielectric properties of subjects with chronic kidney disease (CKD) and normal subjects, at microwave frequency between 1 GHz and 50 GHz using support vector machine (SVM). The dielectric properties of urine were measured at room temperature (25°C), 30°C...
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| Published in | The Journal of microwave power and electromagnetic energy Vol. 50; no. 3; pp. 201 - 213 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Taylor & Francis
02.07.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0832-7823 |
| DOI | 10.1080/08327823.2016.1230927 |
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| Abstract | In this study, we aim to classify the urinary dielectric properties of subjects with chronic kidney disease (CKD) and normal subjects, at microwave frequency between 1 GHz and 50 GHz using support vector machine (SVM). The dielectric properties of urine were measured at room temperature (25°C), 30°C and body temperature (37°C). Urinary dielectric behaviour differences were observed between respective diabetic kidney disease (DKD) and non-DKD compared to normal subjects. Two-group classifications obtained the highest accuracy of 75.91% and 70.02%, respectively, in differentiating DKD and non-DKD group from normal group. The highest classification accuracy was achieved at 63.94% for three-group classifications. The best classification accuracies were obtained at 30°C for two-group and three-group classifications. |
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| AbstractList | In this study, we aim to classify the urinary dielectric properties of subjects with chronic kidney disease (CKD) and normal subjects, at microwave frequency between 1 GHz and 50 GHz using support vector machine (SVM). The dielectric properties of urine were measured at room temperature (25°C), 30°C and body temperature (37°C). Urinary dielectric behaviour differences were observed between respective diabetic kidney disease (DKD) and non-DKD compared to normal subjects. Two-group classifications obtained the highest accuracy of 75.91% and 70.02%, respectively, in differentiating DKD and non-DKD group from normal group. The highest classification accuracy was achieved at 63.94% for three-group classifications. The best classification accuracies were obtained at 30°C for two-group and three-group classifications. |
| Author | Mirhassani, Seyed Mostafa Mun, Peck Shen Ong, Teng Aik Ting, Hua Nong Wong, Chew Ming Chong, Yip Boon |
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| Title | Prediction of chronic kidney disease using urinary dielectric properties and support vector machine |
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