Published on in Vol 8 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49113, first published .
A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study

A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study

A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study

Journals

  1. Pande H, Kapse S, Krishnan V, Kausley S, Satyavathi C, Rai B. Prediction of Shelf Life of Pearl Millet Flour Based on Rancidity and Nutritional Indicators Using a Long Short-Term Memory Network Model. ACS Food Science & Technology 2024;4(3):786 View
  2. 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
  3. Gardner D, Saboo B, Kesavadev J, Mustafa N, Villa M, Mahoney E, Bajpai S. Digital Health Technology in Diabetes Management in the Asia–Pacific Region: A Narrative Review of the Current Scenario and Future Outlook. Diabetes Therapy 2025 View
  4. Lin B, Liu J, Li K, Zhong X. Predicting the Risk of HIV Infection and Sexually Transmitted Diseases Among Men Who Have Sex With Men: Cross-Sectional Study Using Multiple Machine Learning Approaches. Journal of Medical Internet Research 2025;27:e59101 View