Cover Image

Predictors of Childhood Vaccination Uptake in England: An Explainable Machine Learning Analysis of Longitudinal Regional Data (2021-2024)

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Noroozi, Amin, Esha, Sidratul Muntaha, Ghari, Mansoureh
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya
by: Bach, Jimmy, et al.
Published: (2026)

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

Redrawn Maps, Fractured Lives: Lived Experiences of Trauma and Health Crises in 1947 Partition Voices
by: Md Sidratul Muntaha, et al.
Published: (2026)

Budgeted Online Model Selection and Fine-Tuning via Federated Learning
by: Ghari, Pouya M., et al.
Published: (2024)

Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning
by: Ghari, Pouya M., et al.
Published: (2024)

Learning Coupled System Dynamics under Incomplete Physical Constraints and Missing Data
by: Saha, Esha, et al.
Published: (2025)

A Fuzzy-Enhanced Explainable AI Framework for Flight Continuous Descent Operations Classification
by: Noroozi, Amin, et al.
Published: (2025)

An Explainable Pipeline for Machine Learning with Functional Data
by: Goode, Katherine, et al.
Published: (2025)

TeaLeafVision: An Explainable and Robust Deep Learning Framework for Tea Leaf Disease Classification
by: Ahamed, Rafi, et al.
Published: (2026)

Iterative Foundation Model Fine-Tuning on Multiple Rewards
by: Ghari, Pouya M., et al.
Published: (2025)

Explainability of Machine Learning Models under Missing Data
by: Vo, Tuan L., et al.
Published: (2024)

Data Model Design for Explainable Machine Learning-based Electricity Applications
by: Fortuna, Carolina, et al.
Published: (2025)

Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data
by: Pereira, Tomás, et al.
Published: (2026)

UFLUX v2.0: A Process-Informed Machine Learning Framework for Efficient and Explainable Modelling of Terrestrial Carbon Uptake
by: Dong, Wenquan, et al.
Published: (2024)

A Fuzzy Logic-Based Framework for Explainable Machine Learning in Big Data Analytics
by: Yesmin, Farjana, et al.
Published: (2025)

Rethinking Explainable Machine Learning as Applied Statistics
by: Bordt, Sebastian, et al.
Published: (2024)

Hybrid Approach for Driver Behavior Analysis with Machine Learning, Feature Optimization, and Explainable AI
by: Shuvo, Mehedi Hasan, et al.
Published: (2026)

Generalization Bound for Diffusion Models using Random Features
by: Saha, Esha, et al.
Published: (2023)

Verifiable and Provably Secure Machine Unlearning
by: Eisenhofer, Thorsten, et al.
Published: (2022)

Utilizing Large Language Models for Machine Learning Explainability
by: Vassiliades, Alexandros, et al.
Published: (2025)

Efficient Milling Quality Prediction with Explainable Machine Learning
by: Gross, Dennis, et al.
Published: (2024)

Data Cleaning and Machine Learning: A Systematic Literature Review
by: Côté, Pierre-Olivier, et al.
Published: (2023)

Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation
by: Ranaweera, Kanishka, et al.
Published: (2025)

Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis
by: Hasan, Mohammad, et al.
Published: (2024)

Evaluation of Missing Data Analytical Techniques in Longitudinal Research: Traditional and Machine Learning Approaches
by: Tang, Dandan, et al.
Published: (2024)

Learning Causality for Longitudinal Data
by: Bouchattaoui, Mouad EL
Published: (2025)

Machine Learning Meets Transparency in Osteoporosis Risk Assessment: A Comparative Study of ML and Explainability Analysis
by: Elias, Farhana, et al.
Published: (2025)

FoodRL: A Reinforcement Learning Ensembling Framework For In-Kind Food Donation Forecasting
by: Sharma, Esha, et al.
Published: (2025)

Interpretable Machine Learning for Antepartum Prediction of Pregnancy-Associated Thrombotic Microangiopathy Using Routine Longitudinal Laboratory Data
by: Sun, Chuanchuan, et al.
Published: (2026)

An Explainable Machine Learning Approach to Traffic Accident Fatality Prediction
by: Rifat, Md. Asif Khan, et al.
Published: (2024)

Explainable Machine-Learning based Detection of Knee Injuries in Runners
by: Fuentes-Jiménez, David, et al.
Published: (2026)

Investigating the Duality of Interpretability and Explainability in Machine Learning
by: Garouani, Moncef, et al.
Published: (2025)

Feature Importance and Explainability in Quantum Machine Learning
by: Power, Luke, et al.
Published: (2024)

On the Relationship Between Interpretability and Explainability in Machine Learning
by: Leblanc, Benjamin, et al.
Published: (2023)

Explainable Machine Learning for ICU Readmission Prediction
by: de Sá, Alex G. C., et al.
Published: (2023)

Modeling Quantum Machine Learning for Genomic Data Analysis
by: Singh, Navneet, et al.
Published: (2025)

Machine Learning Predictors for Min-Entropy Estimation
by: Blanco-Romero, Javier, et al.
Published: (2024)

Explainability for Machine Learning Models: From Data Adaptability to User Perception
by: Delaunay, julien
Published: (2024)

Explainable Graph-theoretical Machine Learning: with Application to Alzheimer's Disease Prediction
by: Baghirova, Narmina, et al.
Published: (2025)

A Double Machine Learning Approach for the Evaluation of COVID-19 Vaccine Effectiveness under the Test-Negative Design: Analysis of Québec Administrative Data
by: Jiang, Cong, et al.
Published: (2023)