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NCT06988969
Predicting Vaccine Hesitancy Using Machine Learning
trial in Vaccine Refusal in 600 participants. Participants enrolled and being followed up; not accepting new ones.
1 March 2026
Quick facts
| Lead sponsor | University of Yalova |
|---|---|
| Status | Active, enrolled |
| Study type | OBSERVATIONAL |
| Enrollment | 600 |
| Start date | 2 July 2025 |
| Primary completion | 1 March 2026 |
| Estimated completion | 1 August 2026 |
| Sites | 1 location across Turkey (Türkiye) |
Conditions studied
- Vaccine Refusal — all drugs for Vaccine Refusal →
- Vaccine Hesitancy — all drugs for Vaccine Hesitancy →
- Machine Learning — all drugs for Machine Learning →
- Children — all drugs for Children →
Sponsor
University of Yalova
Who can join
Adults 18 to 65, any sex, with Vaccine Refusal or Vaccine Hesitancy. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
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.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT06988969
- Europe PMC full search
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT06988969 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by University of Yalova
- Last refreshed: 18 February 2026
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT06988969.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing