Cover Image

Assessing Alcohol Use Disorder: Insights from Lifestyle, Background, and Family History with Machine Learning Techniques

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Chenlan, Huang, Gaojian, Luo, Yue
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Use of Continuous Glucose Monitoring with Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes
by: Metwally, Ahmed A., et al.
Published: (2025)

Machine Learning Techniques with Fairness for Prediction of Completion of Drug and Alcohol Rehabilitation
by: Roberts-Licklider, Karen, et al.
Published: (2024)

A Comparative Study of Diabetes Prediction Based on Lifestyle Factors Using Machine Learning
by: Nguyen, Bruce, et al.
Published: (2025)

Absolute Risk Prediction for Cannabis Use Disorder in Adolescence and Early Adulthood Using Bayesian Machine Learning
by: Wang, Tingfang, et al.
Published: (2025)

Data Collection and Labeling Techniques for Machine Learning
by: Huang, Qianyu, et al.
Published: (2024)

Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review
by: Razavi, Moein, et al.
Published: (2023)

Local Causal Discovery with Background Knowledge
by: Zheng, Qingyuan, et al.
Published: (2024)

Selecting Interpretability Techniques for Healthcare Machine Learning models
by: Sierra-Botero, Daniel, et al.
Published: (2024)

Addressing Challenges in Time Series Forecasting: A Comprehensive Comparison of Machine Learning Techniques
by: Mortezanejad, Seyedeh Azadeh Fallah, et al.
Published: (2025)

Diabetes Lifestyle Medicine Treatment Assistance Using Reinforcement Learning
by: Tang, Yuhan
Published: (2025)

Integrating White and Black Box Techniques for Interpretable Machine Learning
by: Vernon, Eric M., et al.
Published: (2024)

Advancing Eurasia Fire Understanding Through Machine Learning Techniques
by: Kriuk, Boris
Published: (2025)

Assessing Robustness of Machine Learning Models using Covariate Perturbations
by: R, Arun Prakash, et al.
Published: (2024)

Music Genre Classification Using Machine Learning Techniques
by: Mishra, Alokit, et al.
Published: (2025)

BiPETE: A Bi-Positional Embedding Transformer Encoder for Risk Assessment of Alcohol and Substance Use Disorder with Electronic Health Records
by: Lee, Daniel S., et al.
Published: (2025)

Machine Learning Techniques for Multifactor Analysis of National Carbon Dioxide Emissions
by: Xie, Wenjia, et al.
Published: (2025)

Exploring and Analyzing Wildland Fire Data Via Machine Learning Techniques
by: Dulal, Dipak, et al.
Published: (2023)

Advancing GDP Forecasting: The Potential of Machine Learning Techniques in Economic Predictions
by: Oancea, Bogdan
Published: (2025)

Mitigating Spurious Background Bias in Multimedia Recognition with Disentangled Concept Bottlenecks
by: Huang, Gaoxiang, et al.
Published: (2025)

A Plea for History and Philosophy of Statistics and Machine Learning
by: Lin, Hanti
Published: (2025)

Leveraging Machine Learning and Deep Learning Techniques for Improved Pathological Staging of Prostate Cancer
by: Ghalamkarian, Raziehsadat, et al.
Published: (2025)

Risk Prediction of Cardiovascular Disease for Diabetic Patients with Machine Learning and Deep Learning Techniques
by: Chowdhury, Esha
Published: (2025)

Machine Learning Techniques for Data Reduction of CFD Applications
by: Lee, Jaemoon, et al.
Published: (2024)

Bank Loan Prediction Using Machine Learning Techniques
by: Haque, F M Ahosanul, et al.
Published: (2024)

Traffic Congestion Prediction Using Machine Learning Techniques
by: Yasir, Rafed Muhammad, et al.
Published: (2022)

A Rapid Physics-Informed Machine Learning Framework Based on Extreme Learning Machine for Inverse Stefan Problems
by: Zhuang, Pei-Zhi, et al.
Published: (2025)

Classification of Radiologically Isolated Syndrome and Clinically Isolated Syndrome with Machine-Learning Techniques
by: Mato-Abad, V, et al.
Published: (2024)

Fever Detection with Infrared Thermography: Enhancing Accuracy through Machine Learning Techniques
by: Razmara, Parsa, et al.
Published: (2024)

SHIELD: Securing Healthcare IoT with Efficient Machine Learning Techniques for Anomaly Detection
by: Desai, Mahek, et al.
Published: (2025)

Functional Brain Network Identification in Opioid Use Disorder Using Machine Learning Analysis of Resting-State fMRI BOLD Signals
by: Temtam, Ahmed, et al.
Published: (2024)

Procedural Fairness in Machine Learning
by: Wang, Ziming, et al.
Published: (2024)

Vulnerabilities in Machine Learning-Based Voice Disorder Detection Systems
by: Perelli, Gianpaolo, et al.
Published: (2024)

Optimized IoT Intrusion Detection using Machine Learning Technique
by: Mahmud, Muhammad Zawad, et al.
Published: (2024)

Advances in Machine Learning: Where Can Quantum Techniques Help?
by: Kashyap, Samarth, et al.
Published: (2025)

Pulse Shape Simulation and Discrimination using Machine-Learning Techniques
by: Dutta, Shubham, et al.
Published: (2022)

Machine Learning Techniques for Data Reduction of Climate Applications
by: Li, Xiao, et al.
Published: (2024)

Predicting the Impact of Scope Changes on Project Cost and Schedule Using Machine Learning Techniques
by: Sadeghi, Soheila
Published: (2024)

Enhancing Cryptocurrency Market Forecasting: Advanced Machine Learning Techniques and Industrial Engineering Contributions
by: Pinky, Jannatun Nayeem, et al.
Published: (2024)

First Experiences with the Identification of People at Risk for Diabetes in Argentina using Machine Learning Techniques
by: Rucci, Enzo, et al.
Published: (2024)

Insights From Insurance for Fair Machine Learning
by: Fröhlich, Christian, et al.
Published: (2023)