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Predicting Vaccine Hesitancy Using Machine Learning

NCT06988969 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 600

Last updated 2026-04-29

No results posted yet for this study

Summary

In recent years, emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Virtual Reality (VR) have rapidly become integrated into daily life. The widespread use of these applications has led to the accumulation of vast amounts of data, giving rise to what is commonly referred to as "Big Data." Due to the sheer volume, manual processing and analysis of these large datasets are not feasible. Therefore, software tools and libraries-such as Python and R libraries-have been developed to perform these analyses efficiently and to generate predictions for the future by leveraging historical data through Machine Learning (ML) algorithms.

The primary goal of machine learning algorithms is to discover patterns within existing data and use these patterns to make accurate predictions on new data. The use of machine learning in the field of healthcare has gained significant momentum in recent years. However, a review of the literature reveals that research specifically addressing childhood vaccine hesitancy remains limited.

This study aims to identify the factors contributing to vaccine hesitancy among parents of children aged 0-48 months and to develop a predictive model using machine learning techniques based on these factors. Such a model could help anticipate the likelihood of vaccine refusal among parents and thereby support the development of targeted public health strategies for at-risk populations.

Conditions

  • Vaccine Refusal
  • Vaccine Hesitancy
  • Machine Learning
  • Children

Sponsors & Collaborators

  • University of Yalova

    lead OTHER

Principal Investigators

  • EMEL AVÇİN, Assist Prof · University of Yalova

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-07-02
Primary Completion
2026-03-01
Completion
2026-06-01

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06988969 on ClinicalTrials.gov