Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison

Abstract

Background Uric acid is associated with noncommunicable diseases such as cardiovascular diseases, chronic kidney disease, coronary artery disease, stroke, diabetes, metabolic syndrome, vascular dementia, and hypertension. Therefore, uric acid is considered to be a risk factor for the development of noncommunicable diseases. Most studies on uric acid have been performed in developed countries, and the application of machine-learning approaches in uric acid prediction in developing countries is rare. Different machine-learning algorithms will work differently on different types of data in various diseases; therefore, a different investigation is needed for different types of data to identify the most accurate algorithms. Specifically, no study has yet focused on the urban corporate population in Bangladesh, despite the high risk of developing noncommunicable diseases for this population. Objective The aim of this study was to develop a model for predicting blood uric acid values based on basic health checkup test results, dietary information, and sociodemographic characteristics using machine-learning algorithms. The prediction of health checkup test measurements can be very helpful to reduce health management costs. Methods Various machine-learning approaches were used in this study because clinical input data are not completely independent and exhibit complex interactions. Conventional statistical models have limitations to consider these complex interactions, whereas machine learning can consider all possible interactions among input data. We used boosted decision tree regression, decision forest regression, Bayesian linear regression, and linear regression to predict personalized blood uric acid based on basic health checkup test results, dietary information, and sociodemographic characteristics. We evaluated the performance of these five widely used machine-learning models using data collected from 271 employees in the Grameen Bank complex of Dhaka, Bangladesh. Results The mean uric acid level was 6.63 mg/dL, indicating a borderline result for the majority of the sample (normal range <7.0 mg/dL). Therefore, these individuals should be monitoring their uric acid regularly. The boosted decision tree regression model showed the best performance among the models tested based on the root mean squared error of 0.03, which is also better than that of any previously reported model. Conclusions A uric acid prediction model was developed based on personal characteristics, dietary information, and some basic health checkup measurements. This model will be useful for improving awareness among high-risk individuals and populations, which can help to save medical costs. A future study could include additional features (eg, work stress, daily physical activity, alcohol intake, eating red meat) in improving prediction.

Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms

“…Considering the limited number of studies conducted by using ML in prediction models in dentistry, it is difficult to compare our study with the existing studies. Although most ML-based prediction models used in the field of medicine demonstrated improved performance compared to traditional statistical methods [43][44][45], not all showed a higher performance than regression models developed in these studies. For example, Sampa et al [43] developed a blood uric acid prediction model using multiple ML algorithms and reported that the boosted decision tree model showed improved performance compared to the traditional linear regression model.…”

“…Although most ML-based prediction models used in the field of medicine demonstrated improved performance compared to traditional statistical methods [43][44][45], not all showed a higher performance than regression models developed in these studies. For example, Sampa et al [43] developed a blood uric acid prediction model using multiple ML algorithms and reported that the boosted decision tree model showed improved performance compared to the traditional linear regression model. However, among the ML algorithms used in Sampa's study, the model using a neural network exhibited a lower performance than the linear regression model.…”

“…This shows that not all ML-based prediction models show higher performance than traditional regression models, and the outcome varies based on the variable characteristics, the database used, and disease characteristics to be predicted. Nevertheless, there is no doubt that ML algorithms such as XGBoost and random forest are very powerful classifiers in many cases [43][44][45].…”

An Online Cursive Handwritten Medical Words Recognition System for Busy Doctors in Developing Countries for Ensuring Efficient Healthcare Service Delivery

“…After data augmentation, the vectors need to be regenerated for the new augmented images by updating the parameters. For the rotated images, the values are updated by adding θ with ∆x i and ∆y i , equation (10). To update the vector for parallel-shifting, dx and dy is added to x i and y i , equation (11).…”

Hyperuricemia Prediction Using Photoplethysmogram and Arteriograph

“…In addition to ba-PWV, machine learning (ML) is a promising tool in prediction and classification, especially in various applications in a clinical setup. ML approaches have been used in predicting UA level with sociodemographic characteristics, clinical measurements, and dietary information as the inputs [4,13,14]. Performance was measured in terms of area under the curve (AUC), root mean square error (RMSE), sensitivity (Se), and specificity (Sp).…”