Open Access

ARTICLE

Hyperuricemia Prediction Using Photoplethysmogram and Arteriograph

by Hafifah Ab Hamid¹, Nazrul Anuar Nayan^1,*, Mohd Zubir Suboh¹, Nurin Izzati Mohamad Azizul¹, Mohamad Nazhan Mohd Nizar¹, Amilia Aminuddin², Mohd Shahrir Mohamed Said³, Saharuddin Ahmad⁴

1 Department of Electrical, Electronic and Systems, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia
2 Department of Physiology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, 56000, Malaysia
3 Department of Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, 56000, Malaysia
4 Family Medicine Department, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, 56000, Malaysia

* Corresponding Author: Nazrul Anuar Nayan. Email:

(This article belongs to the Special Issue: Advances in Artificial Intelligence and Machine learning in Biomedical and Healthcare Informatics)

Computers, Materials & Continua 2022, 71(1), 287-304. https://doi.org/10.32604/cmc.2022.021987

Received 22 July 2021; Accepted 23 August 2021; Issue published 03 November 2021

Abstract

Hyperuricemia is an alarming issue that contributes to cardiovascular disease. Uric acid (UA) level was proven to be related to pulse wave velocity, a marker of arterial stiffness. A hyperuricemia prediction method utilizing photoplethysmogram (PPG) and arteriograph by using machine learning (ML) is proposed. From the literature search, there is no available papers found that relates PPG with UA level even though PPG is highly associated with vessel condition. The five phases in this research are data collection, signal preprocessing including denoising and signal quality indexes, features extraction for PPG and SDPPG waveform, statistical analysis for feature selection and classification of UA levels using ML. Adding PPG to the current arteriograph able to reduce cost and increase the prediction performance. PPG and arteriograph data were measured from 113 subjects, and 226 sets of data were collected from the left and right hands of the subjects. The performance of four types of ML, namely, artificial neural network (ANN), linear discriminant analysis (LDA), k-nearest neighbor (kNN), and support vector machine (SVM) in predicting hyperuricemia was compared. From the total of 98 features extracted, 16 features of which showed statistical significance for hyper and normouricemia. ANN gives the best performance compared to the other three ML techniques with 91.67%, 95.45%, and 94.12% for sensitivity, specificity, and accuracy, respectively. Features from PPG and arteriograph able to be used to predict hyperuricemia accurately and noninvasively. This study is the first to find the relationship of PPG with hyperuricemia. It shows a significant relation between PPG signals and arteriograph data toward the UA level. The proposed method of UA prediction shows its potential for noninvasive preliminary assessment.

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