Microwave dielectric property based classification of renal calculi: Application of a kNN algorithm

Banu Saçlı, Cemanur Aydınalp, Gökhan Cansız, Sulayman Joof, Tuba Yilmaz*, Mehmet Çayören, Bülent Önal, Ibrahim Akduman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)

Abstract

The proper management of renal lithiasis presents a challenge, with the recurrence rate of the disease being as high as 46%. To prevent recurrence, the first step is the accurate categorization of the discarded renal calculi. Currently, the discarded renal calculi type is determined with the X-ray powder diffraction method which requires a cumbersome sample preparation. This work presents a new approach that can enable fast and accurate classification of discarded renal calculi with minimal sample preparation requirements. To do so, first, the measurements of the dielectric properties of naturally formed renal calculi are collected with the open-ended contact probe technique between 500 MHz and 6 GHz with 100 MHz intervals. Cole–Cole parameters are fitted to the measured dielectric properties with the generalized Newton–Raphson method. The renal calculi types are classified based on their Cole–Cole parameters as calcium oxalate, cystine, or struvite. The classification is performed using k-nearest neighbors (kNN) machine learning algorithm with the 10 nearest neighbors, where accuracy as high as 98.17% is achieved.

Original languageEnglish
Article number103366
JournalComputers in Biology and Medicine
Volume112
DOIs
Publication statusPublished - Sept 2019

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Ltd

Funding

This work was partially supported by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 750346 .

FundersFunder number
European Union's Horizon 2020 Research and Innovation Program
Marie Sklodowska-Curie
Horizon 2020 Framework Programme750346

    Keywords

    • Classification of kidney stones
    • Cole–Cole parameters
    • Dielectric properties of renal calculi
    • Kidney stone
    • Machine learning
    • Open-ended coaxial probe
    • k-nearest neighbors

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