Izvorni znanstveni članak

Machine Learning Based Analysis of Biochemical and Morphologic Parameters in Patients with Dialysis Related Amyloidosis

Igor Barišić ; Department of Diagnostical and Interventional Radiology, Clinical Hospital Split, Šoltanska 1, 21000 Split, Croatia
Vladimir Wilhelm ; Division of Nephrology, Department of Internal Medicine, Clinical Hospital Split, Šoltanska 1, 21000 Split, Croatia
Nikola Štambuk orcid.org/0000-0002-7438-8090 ; Rudjer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia
Ksenija Karaman ; Department of Ophthalmology, Clinical Hospital Split, Šoltanska 1, 21000 Split, Croatia
Stipan Janković ; Department of Diagnostical and Interventional Radiology, Clinical Hospital Split, Šoltanska 1, 21000 Split, Croatia
Paško Konjevoda ; Rudjer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia
Biserka Pokrić ; Rudjer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia

Sažetak

Dialysis related amyloidosis is the accumulation and deposition of P2-microglobulin derived fibrils in bones and joints, due to insufficient elimination during therapy or slowly progressing renal failure. The aim of this Study was to analyse biochemical, morphologic and anamnestic parameters that may be relevant for the onset and developement of dialysis related amyloidosis. In addition to standard statistical procedures, we also applied the machine-learning based methods of data mining to quantify the risk factors for asymptomatic patients. Extraction of risk factors for the onset of the dialysis related amyloidosis syndrome could enable the clinician to predict the symptoms and consider medical procedures to prevent the onset of the disease. The C4.5 machine learning algorithm extracted a simple and highly accurate tree for discrimination of asymptomatic and symptomatic patients suffering from dialysis related amyloidosis. It remains an open question if our findings may contribute to the problem of accurately predicting the onset of dialysis related arthropathy in the asymptomatic patient group.

Ključne riječi

dialysis; amyloidosis; biochemistry; morphologic parameters; shoulder; knee; symptoms

Hrčak ID:

131755

URI

https://hrcak.srce.hr/131755

Datum izdavanja:

4.11.2002.

Posjeta: 781 *