Primary Care

Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques.

Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied n...

Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment.

INTRODUCTION: Treatment of type 2 diabetes (T2D) remains a significant challenge because of its multifactorial nature and complex metabolic pathways. There is growing interest in finding new therapeutic targets that could lead to safer and more effective treatment options. Takeda G protein-coupled receptor 5 (TGR5) is a promising antidiabetic target that plays a key role in metabolic regulation, e...

Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study.

OBJECTIVES: Sufficient attention has not been given to machine learning (ML) models using longitudinal data for investigating important predictors of ...

CT-Based Body Composition Measures and Systemic Disease: A Population-Level Analysis Using Artificial Intelligence Tools in Over 100,000 Patients.

CT-based abdominal body composition measures have shown associations with important health outcomes. Advances in artificial intelligence (AI) now all...

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.

BACKGROUND: Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell...

A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involve...

Leveraging AI to improve disease screening among American Indians: insights from the Strong Heart Study.

Screening tests for disease have their performance measured through sensitivity and specificity, which inform how well the test can discriminate betwe...

Exploring the effect of gestational diabetes mellitus on retinal vascular morphology by PKSEA-Net.

BACKGROUND: Gestational diabetes mellitus (GDM) is a temporary metabolic disorder in which small retinal vessels may have experience subtle changes be...

Comprehensive bioinformatics analysis identifies metabolic and immune-related diagnostic biomarkers shared between diabetes and COPD using multi-omics and machine learning.

BACKGROUND: Diabetes and chronic obstructive pulmonary disease (COPD) are prominent global health challenges, each imposing significant burdens on aff...

Nationwide real-world implementation of AI for cancer detection in population-based mammography screening.

Artificial intelligence (AI) in mammography screening has shown promise in retrospective evaluations, but few prospective studies exist. PRAIM is an o...

A Comprehensive Review of Artificial Intelligence (AI) Applications in Pulmonary Hypertension (PH).

Pulmonary hypertension (PH) is a complex condition associated with significant morbidity and mortality. Traditional diagnostic and management approac...

Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning.

Polycystic ovary syndrome (PCOS) is a primary endocrine disorder affecting premenopausal women involving metabolic dysregulation. We aimed to screen s...

A data-driven machine learning algorithm to predict the effectiveness of inulin intervention against type II diabetes.

INTRODUCTION: The incidence of type 2 diabetes mellitus (T2DM) has increased in recent years. Alongside traditional pharmacological treatments, nutrit...

Deep learning captures the effect of epistasis in multifactorial diseases.

BACKGROUND: Polygenic risk score (PRS) prediction is widely used to assess the risk of diagnosis and progression of many diseases. Routinely, the weig...

Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets.

INTRODUCTION: Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases do...

Multimodal machine learning for analysing multifactorial causes of disease-The case of childhood overweight and obesity in Mexico.

BACKGROUND: Mexico has one of the highest global incidences of paediatric overweight and obesity. Public health interventions have shown only moderate...

Predicting and Ranking Diabetic Ketoacidosis Risk Among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.

To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-rela...

Machine learning discovery of novel antihypertensive peptides from highland barley protein inhibiting angiotensin I-converting enzyme (ACE).

Hypertension is a major global health concern, and there is a need for new antihypertensive agents derived from natural sources. This study aims to id...

Accelerated Screening of Highly Sensitive Gas Sensor Materials for Greenhouse Gases Based on DFT and Machine Learning Methods.

Greenhouse gases (GHGs) have caused great harm to the ecological environment, so it is necessary to screen gas sensor materials for detecting GHGs. In...

A novel RFE-GRU model for diabetes classification using PIMA Indian dataset.

Diabetes is a long-term condition characterized by elevated blood sugar levels. It can lead to a variety of complex disorders such as stroke, renal fa...