AI model detects prediabetes using ECG data without need for blood tests

DiaCardia, a novel artificial intelligence model that can accurately identify individuals with prediabetes using either ...

Machine learning can predict patients' responses to antidepressants—while disentangling drug and placebo effects

Depression is one of the most widespread mental health disorders worldwide, affecting approximately 4% of the global ...
IEEE

Improving Accuracy of Diabetes Prediction Using Machine Learning Techniques and Clinical Data

Abstract: Diabetes is a chronic condition affecting millions of people globally, requiring early and accurate prediction to mitigate associated health risks. This study employed a Kaggle dataset with ...
Open Heart

Machine learning-based cardiovascular risk calculator for non-cardiac surgery

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...
Frontiers

Prediction of total iodine dose of I-131 therapy for Graves’ hyperthyroidism achieved remission status: a random forest regressor model approach to assess treatment efficacy

Department of Nuclear Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi Zhuang Autonomous Region, China Objective: Graves’ hyperthyroidism (GH) presents significant ...
IEEE

Obesity Health Risk Prediction using Random Forest and SVM Algorithms

Abstract: Obesity-related health dangers have become a serious global issue that requires adequately pinpointing and quickly preventing health problems. One of the research studies in the article ...