Primary Care

Web application using machine learning to predict cardiovascular disease and hypertension in mine workers.

This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest alg...

Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival.

Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence. The chances of treating the Recurrence of cervical carcinoma arelimited. The main objective of a research is to find the key features that will predict the cervical cancer recurrence and survival ra...

Robust self management classification via sparse representation based discriminative model for mild cognitive impairment associated with diabetes mellitus.

Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerab...

Cerebral compliance assessment from intracranial pressure waveform analysis: Is a positional shift-related increase in intracranial pressure predictable?

Real-time monitoring of intracranial pressure (ICP) is a routine part of neurocritical care in the management of brain injury. While mainly used to de...

Integration of Optical Coherence Tomography Images and Real-Life Clinical Data for Deep Learning Modeling: A Unified Approach in Prognostication of Diabetic Macular Edema.

The primary ocular effect of diabetes is diabetic retinopathy (DR), which is associated with diabetic microangiopathy. Diabetic macular edema (DME) ca...

Estimating cardiovascular mortality in patients with hypertension using machine learning: The role of depression classification based on lifestyle and physical activity.

PURPOSE: This study aims to harness machine learning techniques, particularly the Random Survival Forest (RSF) model, to assess the impact of depressi...

A machine learning tool for early identification of celiac disease autoimmunity.

Identifying which patients should undergo serologic screening for celiac disease (CD) may help diagnose patients who otherwise often experience diagno...

Clustering and classification for dry bean feature imbalanced data.

The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification perf...

Predicting host health status through an integrated machine learning framework: insights from healthy gut microbiome aging trajectory.

The gut microbiome, recognized as a critical component in the development of chronic diseases and aging processes, constitutes a promising approach fo...

Diabetes and Cataracts Development-Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study.

Diabetes has become a global epidemic, contributing to significant health challenges due to its complications. Among these, diabetes can affect sight...

Adoption of Digital Therapeutics in Europe.

Digital therapeutics (DTx) are an emerging medical therapy comprising evidence-based interventions that are regulatory approved for patient use, or ar...

Methodology for Safe and Secure AI in Diabetes Management.

The use of artificial intelligence (AI) in diabetes management is emerging as a promising solution to improve the monitoring and personalization of th...

Heterogeneous blood pressure treatment effects on cognitive decline in type 2 diabetes: A machine learning analysis of a randomized clinical trial.

AIM: We aimed to identify the characteristics of patients with diabetes who can derive cognitive benefits from intensive blood pressure (BP) treatment...

StUdy of Gestational diabetes And Risk using Electronic Data (SUGARED): a population-based cohort study-study protocol.

INTRODUCTION: The incidence of gestational diabetes mellitus (GDM) in Australia has tripled in the last 20 years. Consequently, over 40 000 pregnancie...

A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort.

Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical present...

An overview of artificial intelligence based automated diagnosis in paediatric dentistry.

Artificial intelligence (AI) is a subfield of computer science with the goal of creating intelligent machines (1) Machine learning is a branch of arti...

Evaluation of ChatGPT-4 Performance in Answering Patients' Questions About the Management of Type 2 Diabetes.

OBJECTIVES: Type 2 diabetes mellitus is a disease with a rising prevalence worldwide. Person-centered treatment factors, including comorbidities and t...

Adipsin and Leptin Levels in Type 2 Diabetic Patients on Sitagliptin and Metformin Versus Metformin Therapy.

OBJECTIVES: Adipsin and leptin are adipokines that link adipose tissue dysfunction and increased fat accumulation to obesity-related metabolic disorde...

Improving search strategies in bibliometric studies on machine learning in renal medicine.

This paper evaluated the bibliometric study by Li et al. (Int Urol Nephrol, 2024) on machine learning in renal medicine. Although the study claims to ...

Breath Analyzer for Real-Time Exercise Fat Burning Prediction: Oral and Alveolar Breath Insights with CNN.

The increasing prevalence of obesity and metabolic disorders has created a significant demand for personalized devices that can effectively monitor fa...