This paper is in the following e-collection/theme issue:

Machine Learning (1501) Clinical Information and Decision Making (1434) Registered Report (579) Decision Support for Health Professionals (1210)

Published on in Vol 8, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18331, first published .
Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison

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

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

Authors of this article:

Masuda Begum Sampa1 Author Orcid Image ;   Md Nazmul Hossain2 Author Orcid Image ;   Md Rakibul Hoque3 Author Orcid Image ;   Rafiqul Islam4 Author Orcid Image ;   Fumihiko Yokota5 Author Orcid Image ;   Mariko Nishikitani4 Author Orcid Image ;   Ashir Ahmed1 Author Orcid Image
Article Authors Cited by (14) Tweetations Metrics

Journals

  1. Park Y, Kim S, Choi Y. Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms. International Journal of Environmental Research and Public Health 2021;18(16):8613 View
  2. Kumar Dubey A, Gupta U, Jain S. Hyperuricemia Prediction Using Photoplethysmogram and Arteriograph. Computers, Materials & Continua 2022;71(1):287 View
  3. Tabassum S, Abedin N, Rahman M, Rahman M, Ahmed M, Islam R, Ahmed A. An online cursive handwritten medical words recognition system for busy doctors in developing countries for ensuring efficient healthcare service delivery. Scientific Reports 2022;12(1) View
  4. Usman A, Tanimu A, Abba S, Isik S, Aitani A, Alasiri H. Feasibility of the Optimal Design of AI-Based Models Integrated with Ensemble Machine Learning Paradigms for Modeling the Yields of Light Olefins in Crude-to-Chemical Conversions. ACS Omega 2023;8(43):40517 View
  5. Sampa M, Biswas T, Rahman M, Aziz N, Hossain M, Aziz N. A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study. JMIR Diabetes 2023;8:e49113 View
  6. Chen Q, Hu H, She Y, He Q, Huang X, Shi H, Cao X, Zhang X, Xu Y. An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus. Scientific Reports 2024;14(1) View
  7. Paplomatas P, Rigas D, Sergounioti A, Vrahatis A. Enhancing Metabolic Syndrome Detection through Blood Tests Using Advanced Machine Learning. Eng 2024;5(3):1422 View
  8. Mahfouz M, Mahfouz Y, Harmouche-Karaki M, Matta J, Younes H, Helou K, Finan R, Abi-Tayeh G, Meslimani M, Moussa G, Chahrour N, Osseiran C, Skaiki F, Narbonne J. Utilizing machine learning to classify persistent organic pollutants in the serum of pregnant women: a predictive modeling approach. Environmental Science and Pollution Research 2024;31(40):52980 View
  9. Oyebola K, Ligali F, Owoloye A, Erinwusi B, Alo Y, Musa A, Aina O, Salako B. Machine Learning–Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals. JMIRx Med 2024;5:e56993 View
  10. Alam M, Sajib M, Rahman F, Ether S, Hanson M, Sayeed A, Akter E, Nusrat N, Islam T, Raza S, Tanvir K, Chisti M, Rahman Q, Hossain A, Layek M, Zaman A, Rana J, Rahman S, Arifeen S, Rahman A, Ahmed A. Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review. Journal of Medical Internet Research 2024;26:e54710 View
  11. Liu Y, Nfor O, Zhong J, Lin C, Liaw Y. Risk Factors for Gout in Taiwan Biobank: A Machine Learning Approach. Journal of Inflammation Research 2024;Volume 17:9847 View

Books/Policy Documents

  1. Ahmed A, Hossain F, Abedin N, Islam R, Shah F, Hoshino H. Base of the Pyramid and Business Process Outsourcing Strategies. View