Journal of Ideas in Health Artificial intelligence-powered predictive modeling for calorie burns in underserved regions

Predicting sleep disturbance and healthcare utilisation using survey data

Objective: To evaluate the ability of machine learning classification models to predict sleep disturbance and high healthcare utilisation among older adults using categorical national health survey data. Method and Material: A cross-sectional observational study was conducted using de-identified survey data from 714 ad...

Machine Learning-Based Obesity Prediction and Feature Importance Analysis Using Random Forest

Obesity prevalence continues to rise both globally and in Indonesia, yet most predictive studies rely on datasets from Western populations or clinical settings, leaving a gap in machine-learning-based evidence from Southeast Asian university communities. This study applies Random Forest to survey data from 300 responde...

Evaluasi Model

ARTIFICIAL INTELLIGENCE FOR PREDICTION OF EXERCISE-ASSOCIATED HYPOGLYCEMIA IN PHYSICALLY ACTIVE PEOPLE WITH TYPE 1 DIABETES

Research objectives. Physical activity continues to be a major barrier to achieving ideal blood glucose control in individuals with type 1 diabetes due to the ongoing threat of hypoglycemia. Traditional alerts from Continuous Glucose Monitoring (CGM) systems are mostly reactive as they fail to consider the specific con...

Machine Learning Models Using Electronic Health Record Data to Predict Obstructive Sleep Apnea

Improving 10-year cardiovascular disease risk prediction using automated machine learning.

This study developed an AutoML-based CVD risk prediction model with superior discrimination and calibration, providing clinicians a practical tool for risk stratification, and has the potential to reduce CVD burden at the population level.