Metabolic Syndrome

Precise risk-prediction model including arterial stiffness for new-onset atrial fibrillation using machine learning techniques.

Atrial fibrillation (AF) is the most common clinically significant cardiac arrhythmia and is an important risk factor for ischemic cerebrovascular events. This study used machine learning techniques to develop and validate a new risk prediction model for new-onset AF that incorporated the use electrocardiogram to diagnose AF, data from participants with a wide age range, and considered hypertensio...

Integrated biomarker profiling for predicting the response of type 2 diabetes to metformin.

AIM: To explore biomarkers that can predict the response of type 2 diabetes (T2D) patients to metformin at an early stage to provide better treatment for T2D.

Explainable hypoglycemia prediction models through dynamic structured grammatical evolution.

Effective blood glucose management is crucial for people with diabetes to avoid acute complications. Predicting extreme values accurately and in a tim...

A machine learning analysis of predictors of future hypertension in a young population.

BACKGROUND: Early diagnosis of hypertension (HT) is crucial for preventing end-organ damage. This study aims to identify the risk factors for future H...

Telephone follow-up based on artificial intelligence technology among hypertension patients: Reliability study.

Artificial intelligence (AI) telephone is reliable for the follow-up and management of hypertensives. It takes less time and is equivalent to manual f...

The development of an EU-wide nutrition and physical activity expert knowledge base to support a personalised mobile application across various EU population groups.

A healthy lifestyle comprising regular physical activity and an adequate diet is imperative for the prevention of non-communicable diseases such as hy...

Predictive modelling and identification of key risk factors for stroke using machine learning.

Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. However, addressing hidden risk ...

Machine Learning-Based Mortality Prediction in Chronic Kidney Disease among Heart Failure Patients: Insights and Outcomes from the Jordanian Heart Failure Registry.

Heart failure (HF) is a prevalent and debilitating condition that imposes a significant burden on healthcare systems and adversely affects the qualit...

Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics.

Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have d...

Deep-learning survival analysis for patients with calcific aortic valve disease undergoing valve replacement.

Calcification of the aortic valve (CAVDS) is a major cause of aortic stenosis (AS) leading to loss of valve function which requires the substitution b...

Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging.

Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular di...

A retrospective prognostic evaluation using unsupervised learning in the treatment of COVID-19 patients with hypertension treated with ACEI/ARB drugs.

INTRODUCTION: This study aimed to evaluate the prognosis of patients with COVID-19 and hypertension who were treated with angiotensin-converting enzym...

Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning.

BACKGROUND: Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state ...

A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants.

BACKGROUND: Bronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) remains a devastating clinical complication seriously affecting the ...

Artificial Intelligence-Derived Risk Prediction: A Novel Risk Calculator Using Office and Ambulatory Blood Pressure.

BACKGROUND: Quantification of total cardiovascular risk is essential for individualizing hypertension treatment. This study aimed to develop and valid...

Progression from Prediabetes to Diabetes in a Diverse U.S. Population: A Machine Learning Model.

To date, there are no widely implemented machine learning (ML) models that predict progression from prediabetes to diabetes. Addressing this knowledg...

Prediction of cardiovascular risk factors from retinal fundus photographs: Validation of a deep learning algorithm in a prospective non-interventional study in Kenya.

AIM: Hypertension and diabetes mellitus (DM) are major causes of morbidity and mortality, with growing burdens in low-income countries where they are ...

Text mining of hypertension researches in the west Asia region: a 12-year trend analysis.

More than half of the world population lives in Asia and hypertension (HTN) is the most prevalent risk factor found in Asia. There are numerous articl...

Nutritional management recommendation systems in polycystic ovary syndrome: a systematic review.

BACKGROUND: People with polycystic ovary syndrome suffer from many symptoms and are at risk of developing diseases such as hypertension and diabetes i...

Response accuracy of ChatGPT 3.5 Copilot and Gemini in interpreting biochemical laboratory data a pilot study.

With the release of ChatGPT at the end of 2022, a new era of thinking and technology use has begun. Artificial intelligence models (AIs) like Gemini (...