Primary Care

Obesity

Latest AI and machine learning research in obesity for healthcare professionals.

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Clustering and classification for dry bean feature imbalanced data.

The traditional machine learning methods such as decision tree (DT), random forest (RF), and support...

Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.

Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic a...

Predicting candidate biomarkers for COVID-19 associated with leukemia in children.

Since the COVID-19 pandemic, a significant number of pediatric leukemia patients have shown to have ...

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 ...

Optimizing hypertension prediction using ensemble learning approaches.

Hypertension (HTN) prediction is critical for effective preventive healthcare strategies. This study...

Comparative analysis of the human microbiome from four different regions of China and machine learning-based geographical inference.

The human microbiome, the community of microorganisms that reside on and inside the human body, is c...

Biofilm architecture and dynamics of the oral ecosystem.

The oral cavity, being a nutritionally enriched environment, has been proven to be an ideal habitat ...

Investigating the anti-obesity potential of leaf bioactive compounds through machine learning and computational biology methods.

Obesity, a growing global health concern, is linked to severe ailments such as cardiovascular diseas...

Profiling the AI speaker user: Machine learning insights into consumer adoption patterns.

The objective of this study is to identify the characteristics of users of AI speakers and predict p...

Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants.

OBJECTIVE: To develop and validate a machine learning model incorporating dietary antioxidants to pr...

XGBoost-based nomogram for predicting lymph node metastasis in endometrial carcinoma.

This study aims to construct and optimize risk prediction models for lymph node metastasis (LNM) in ...

Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review.

BACKGROUND: Machine Learning (ML) models have been used to predict common mental disorders (CMDs) an...

Predicting 30-day reoperation following primary total knee arthroplasty: machine learning model outperforms the ACS risk calculator.

The ACS risk calculator (ARC) has proven less effective in predicting patient-specific risk of early...

AcidAGE: a biological age determination neural network based on urine organic acids.

Organic acids reflect the course of all important metabolic processes and the effects of diet, nutri...

Machine learning classification meets migraine: recommendations for study evaluation.

The integration of machine learning (ML) classification techniques into migraine research has offere...

Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective.

Early recognition of risk factors for prolonged mechanical ventilation (PMV) could allow for early c...

Comparative evaluation of ChatGPT-4, ChatGPT-3.5 and Google Gemini on PCOS assessment and management based on recommendations from the 2023 guideline.

CONTEXT: Artificial intelligence (AI) is increasingly utilized in healthcare, with models like ChatG...

Assessing the efficacy of artificial intelligence to provide peri-operative information for patients with a stoma.

BACKGROUND: Stomas present significant lifestyle and psychological challenges for patients, requirin...

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