AIMC Topic: Life Style
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Machine Learning-Based Analysis of Lifestyle Risk Factors for Atherosclerotic Cardiovascular Disease: Retrospective Case-Control Study.
JMIR medical informatics
Aug 7, 2025
BACKGROUND: The risk of developing atherosclerotic cardiovascular disease (ASCVD) varies among individuals and is related to a variety of lifestyle factors in addition to the presence of chronic diseases.
Predicting mortality risk in Alzheimer's disease using machine learning based on lifestyle and physical activity.
Scientific reports
Jul 24, 2025
Alzheimer's disease (AD), a progressive neurodegenerative disorder, significantly impacts patient survival, prompting the need for accurate prognostic tools. Lifestyle factors and physical activity levels have been identified as critical modifiable r...
Multidomain Behavioral Change Digital Coaching for Chronic Disease Management in Patients With Type 2 Diabetes: Framework Development and Preliminary Evaluation.
JMIR formative research
Jul 9, 2025
BACKGROUND: Unhealthy lifestyle behaviors have been identified as a major cause of numerous health issues, with a steady global increase in their prevalence. Addressing this challenge requires comprehensive behavioral changes to promote the adoption ...
Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015-2018.
BMC public health
Jul 3, 2025
Cardiometabolic Multimorbidity (CMM) has emerged as one of the primary threats to human health globally due to its high incidence, disability, and mortality rates. Accurate identification of CMM patterns is crucial for CMM classification and health m...
Development and Validation of a Lifestyle-Based 10-Year Risk Prediction Model of Colorectal Cancer for Early Stratification: Evidence from a Longitudinal Screening Cohort in China.
Nutrients
May 31, 2025
Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with growing evidence linking risk to lifestyle and dietary factors. However, nutrition-related exposures have rarely been integrated into existing CRC ...
Type 2 Diabetes in Taiwan: Unmasking Influential Factors Through Advanced Predictive Modeling.
Journal of diabetes research
May 27, 2025
Type 2 diabetes (T2D) is influenced by lifestyle, genetics, and environmental conditions. By utilizing machine learning techniques, we can enhance the precision of T2D risk prediction by analyzing the complex interactions among these variables. This...
Unraveling MASLD: The Role of Gut Microbiota, Dietary Modulation, and AI-Driven Lifestyle Interventions.
Nutrients
May 4, 2025
Gut microbiota has a crucial role in the pathophysiology of metabolic-associated steatotic liver disease (MASLD), influencing various metabolic mechanisms and contributing to the development of the disease. Dietary interventions targeting gut microbi...
Promoting active health with AI technologies: Current status and prospects of high-altitude therapy, simulated hypoxia, and LLM-driven lifestyle rehabilitation approaches.
Bioscience trends
Apr 29, 2025
In the context of the rising global prevalence of obesity, traditional intervention measures have proven insufficient to meet the demands of personalized and sustainable health management, necessitating the exploration of innovative solutions through...
Hybrid Neural network and machine learning models with improved optimization method for gut microbiome effects on the sleep quality in patients with endometriosis.
Computer methods and programs in biomedicine
Apr 26, 2025
BACKGROUND AND OBJECTIVE: Endometriosis is a chronic gynecological condition known to affect the quality of life of millions of women globally, often manifesting with symptoms that impact sleep quality. Emerging evidence suggests a crucial role of th...
Artificial intelligence models utilize lifestyle factors to predict dry eye related outcomes.
Scientific reports
Apr 18, 2025
The purpose of this study is to examine and interpret machine learning models that predict dry eye (DE)-related clinical signs, subjective symptoms, and clinician diagnoses by heavily weighting lifestyle factors in the predictions. Machine learning m...
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