AIMC Topic: Life Style

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Type 2 Diabetes in Taiwan: Unmasking Influential Factors Through Advanced Predictive Modeling.

Journal of diabetes research
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
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
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
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
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...

Dietary and lifestyle determinants of vitamin D status in the UK Biobank Cohort study for predictive modeling.

The Journal of nutritional biochemistry
Vitamin D (VD) is involved in a wide variety of physiological processes. The high prevalence of VD deficiency in the population requires stronger preventive measures. The aim was to characterize the dietary and lifestyle determinants of VD levels in ...

Early obesity risk prediction via non-dietary lifestyle factors using machine learning approaches.

Clinical obesity
Obesity poses a significant health threat, contributing to the development of noncommunicable diseases (NCDs). Early identification of individuals at higher risk for obesity is crucial for implementing effective prevention strategies. This study expl...

Capsule DenseNet++: Enhanced autism detection framework with deep learning and reinforcement learning-based lifestyle recommendation.

Computers in biology and medicine
Autism Spectrum Disorder (ASD) is a complex neurological condition that impairs the ability to interact, communicate, and behave. It is becoming increasingly prevalent worldwide, with an increase in the number of young children diagnosed with ASD in ...

Tlalpan 2020 Case Study: Enhancing Uric Acid Level Prediction with Machine Learning Regression and Cross-Feature Selection.

Nutrients
Uric acid is a key metabolic byproduct of purine degradation and plays a dual role in human health. At physiological levels, it acts as an antioxidant, protecting against oxidative stress. However, excessive uric acid can lead to hyperuricemia, cont...