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.
Co-design and co-production with non-academic stakeholders has been recognized as a key approach in transdisciplinary sustainability research. The majority of transdisciplinary studies have been conducted in Europe and North America, with a marked lack of such research in the Asian context-particularly with regard to healthcare. Utilizing a case study involving mobile health check-ups performed using a portable health clinic system in Jaipur, India, from March 2016 to March 2018, this study identifies key factors in co-design and co-production that should be considered to ensure the project's sustainability. Thoroughly reviewing all of the documents and materials related to the case study's co-design and co-production, this study identifies the following key factors:(1) mutual stakeholder agreement on a long-term research plan, protocol, and budget; (2) harmonizing research objectives, frames, and the scale of stakeholder expectations; (3) stakeholders' commitment and a sense of ownership derived from their needs and priorities; (4) stakeholder trust; (5) effective coordinators; (6) personality type and characteristics of stakeholder leaders; (7) capacity building and the empowerment of local research staff and participants; and (8) continuous efforts to involve stakeholders throughout the co-design and co-production processes. Facilitating effective co-design and co-production, these factors will help ensure the future sustainability of projects.Sustainability 2018, 10, 4148 2 of 16 research [1]. As such, adopted approaches tend to involve a mutual learning process and joint partnerships between interdisciplinary scholars and non-academic stakeholders from the fields of science, industry, politics, technology, and civil society [1,8,9]. Exemplifying the practical utility of such an approach, an international research program called "Future Earth" was launched at the United Nations (UN) Conference on Sustainable Development in June 2012. Incorporating the natural and social sciences to solve global environmental issues [10], Future Earth has "pioneered approaches to the co-design and co-production of solutions-oriented, transdisciplinary research for global sustainable development" [11].However, transdisciplinary research remains hampered in a number of ways. First, the most recent reviews of transdisciplinary case studies have concluded that the methods and concepts of co-design and co-production processes lack clarity [1,2,4,12]. Second, the majority of extant transdisciplinary research only describes the early stages of co-design, rather than its actual implementation and application [2,7]. Indeed, which studies include key components of early co-design-such as the "framing of problems" [4,12,13], "social capital and partnerships with mutual trust" [14-16], "scaling" [17], "accountability" [9,18], "ownership" [18], as well as "priorities and needs" [1,9]-few have used case studies to discuss both co-design and co-production in a comprehensive, bottom-up manner [4]. Third, in comparison to Europe and North Amer...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.