Application of SVM and k-NN classifiers in medical diagnostics

What is it about?

The paper presents an innovative method for the diagnosis of the arterio-venous fistula based on recorded acoustic signals. A fistula is an artificial connection between an artery and a vein made to obtain a suitably large blood flow for haemodialysis. If the fistula does not work properly, thrombosis or other health- or life-threatening conditions may develop. Based on the analysis of sound generated by blood flowing through the fistula, the occurrence of pathological conditions may be diagnosed. The study were performed using a data set containing samples of the sound signal emitted by the arteriovenous fistula. The aim was to create a solution with multiclass classification based on the SVM and k-NN classifiers family allowing for effective and credible assessment of the state of arterial-venous fistula.

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Why is it important?

A properly functioning arteriovenous fistula (AVF) is the best vascular access in patients on chronic haemodialysis (HP). Diagnosing a deterioration of the AVF function in the pre-clinical state would prevent HP from invasive repair procedures.