Early stage of chronic kidney disease by using statistical evaluation of the previous measurement results

• Autorzy: Akben S. B.

Identyfikatory

DOI

10.1016/j.bbe.2016.08.004

• Języki publikacji: EN

Abstrakty

EN

Chronic kidney disease (CKD) that causes the progressive losses in kidney functions is one of the major public health problems. Expert medical doctors can diagnose the CKD through symptoms, blood and urine tests in its early stages. However, the diagnosis of CKD might be difficult for expert medical doctors in case of the questionable measurement result. Therefore a new mathematical method that would be helpful to the expert medical doctors is required. It can be said that there is no studies related with automatic diagnosis of CKD in the literature. This study aims to remedy this shortcoming in the literature. In this study, for each of test and symptom values, averages of measurement results were calculated separately for healthy subjects and patients. Then the measured values were marked as ‘‘0’’ or ‘‘1’’ (healthy or patient) according to closeness to average values. Finally, the classification was performed by averaging the values marked for each subject. The success rate of the proposed method is between 96% and 98% according to the age ranges. In conclusion section of the study, how to implement the proposed method in real life is offered.

Słowa kluczowe

EN

chronic kidney disease; chronic renal failure; statistical evaluation; signal processing; CKD

PL

przewlekła choroba nerek; ocena statystyczna; przetwarzanie sygnału

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2016
• Tom: Vol. 36, no. 4
• Strony: 626--631
• Opis fizyczny: Bibliogr. 18 poz., rys., tab., wykr.

Twórcy

autor

Akben S. B.

• Bahce Vocational School, Osmaniye Korkut Ata University, Turkey

Bibliografia

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• [15] Hallan SI, Ritz E, Lydersen S, Romundstad S, Kvenild K, Orth SR. Combining GFR and albuminuria to classify CKD improves prediction of ESRD. J Am Soc Nephrol 2009;20 (5):1069–77.
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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.