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 ...
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 ...
Scientific Research Publishing

Wager, S. and Athey, S. (2018) Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests. Journal of the American Statistical Association, 113, 1228-1242.

ABSTRACT: Special education services are designed to provide tailored support for students with diverse learning needs, with the expectation of improving academic achievement. This study examines the ...
BMJ

Development and validation of a machine learning-based prediction model for frailty in older adults with diabetes: a study protocol for a retrospective cohort study

Introduction Frailty is a common condition in older adults with diabetes, which significantly increases the risk of adverse health outcomes. Early identification of frailty in this population is ...
Frontiers

Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction

Background: Early identification of Type 1 Diabetes Mellitus (T1DM) in pediatric populations is crucial for implementing timely interventions and improving long-term outcomes. Peripheral blood ...
IEEE

Diabetes Prediction with Random Forest - Logistic Regression algorithm using voting classifier

Abstract: A commonly recognized chronic metabolic condition known as Diabetes mellitus significantly affects the global, social and economic standing of people. Obesity, age, high blood pressure, ...
Frontiers

A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008–2012, 2017–2021)

Background: Machine learning technology that uses available clinical data to predict diabetic retinopathy (DR) can be highly valuable in medical settings where fundus cameras are not accessible.
IEEE

Random Forest Algorithm for the Prediction of Diabetes

Abstract: Diabetes is taken into account together of the deadliest and chronic disease that causes a rise in glucose. Polygenic disease is that the kind wherever the exocrine gland doesn't manufacture ...