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.
Highlights
Cardiovascular disease (CVD) contributed approximately 18.6 million deaths globally in 2019; it increases to approximately 53.7% from 1990 [1]
The relationship between each significant PPG and second derivative of PPG (SDPPG) features may be further studied in accordance with PWV parameter, which was related to uric acid (UA) level [55]
We found that artificial neural network (ANN) produced better performance than the other machine learning (ML) used in previous studies [4,12,13]
Summary
Cardiovascular disease (CVD) contributed approximately 18.6 million deaths globally in 2019; it increases to approximately 53.7% from 1990 [1]. This finding may be attributed to the escalation of obesity, diabetes, hypertension, dyslipidemia, and hyperuricemia [2]. According to a study by Touserkani et al [3] and Lee et al [4], the risk of CVD mortality was associated significantly with high uric acid (UA) level for males and females. Even though the elevation of UA level as an independent risk factor of CVD risk still remains debatable, a study from Chang et al [5] showed that UA level is associated significantly with cardiovascular risk. The gold standard in measuring UA level is through micro-invasive blood test, which causes discomfort and difficulty in continuous monitoring
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