AIMC Topic: Risk Factors

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Integrating inflammatory biomarkers and demographic variables with machine learning to predict endometriosis risk.

Scientific reports
This study explores the relationship between inflammatory biomarkers and the risk of endometriosis, aiming to develop a predictive model using National Health and Nutrition Examination Survey (1999-2006) data. The dataset included 4,089 females with ...

Development and validation of nomogram and machine learning models to predict sarcopenia in patients with chronic kidney disease.

Scientific reports
Chronic kidney disease (CKD) is a growing public health problem worldwide. CKD not only leads to renal function decline but also increases the risk of multiple complications, sarcopenia being particularly common and severe. At present, there is a lac...

Prioritization of patients at risk of heart attack using a novel full-objective ITARA based on Random Forest and Decision tree.

Scientific reports
Heart attacks remain a major cause of morbidity and mortality, particularly among middle-aged and older adults, often aggravated by unhealthy lifestyles and limited preventive care. Early identification and prioritization of at-risk individuals are e...

AI-Powered Thermography for Diabetic Foot Risk Stratification: Multicenter Cross-Sectional Study.

JMIR formative research
BACKGROUND: Diabetic foot complications are among the most severe and costly outcomes associated with diabetes, with high prevalence particularly in the Middle East and North Africa region. Current screening tools are often limited by subjectivity, i...

Risk Prediction of Major Adverse Cardiovascular Events Within One Year After Percutaneous Coronary Intervention in Patients With Acute Coronary Syndrome: Machine Learning-Based Time-to-Event Analysis.

JMIR medical informatics
BACKGROUND: Patients with acute coronary syndrome (ACS) who undergo percutaneous coronary intervention (PCI) remain at high risk for major adverse cardiovascular events (MACE). Conventional risk scores may not capture dynamic or nonlinear changes in ...

Online machine learning model for predicting delirium risk in elderly patients with chronic kidney disease: development and preliminary validation.

European journal of medical research
BACKGROUND: Delirium frequently complicates elderly chronic kidney disease (CKD) patients due to multifactorial vulnerability. Early detection in geriatric intensive care unit (ICU) settings is challenged by traditional assessments' communication def...

The global epidemiology, risk factors, and mortality prediction of nocardiosis: an easily missed opportunistic infection.

Scientific reports
This study was to comprehensively investigate the epidemiology of nocardiosis worldwide and develop an interpretable machine learning (ML) model to predict mortality in patients with nocardiosis. The PubMed and Web of Science databases were searched ...

Comparison of Machine Learning Models for Colon Cancer Survival: Predictive Modeling Approach.

JMIR cancer
BACKGROUND: Colon cancer is a leading cause of cancer-related deaths worldwide, with survival influenced by risk factors, treatment type, and patient characteristics. Traditional statistical models, such as Kaplan-Meier curves, have been widely used ...

Predicting arthritis risk with machine learning: Insights from the 2023 National Health Interview Survey data.

PloS one
Arthritis, a common chronic disease encompassing multiple subtypes of osteoarthritis and rheumatoid arthritis, was explored in this study as a risk-related factor based on data from the 2023 U.S. National Health Interview Survey (NHIS). The study inc...