{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:24:07Z","timestamp":1772555047196,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685960","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,15]]},"abstract":"<jats:p>Synthetic data, generated using generative AI techniques, closely mimics the characteristics of real data while enhancing privacy for sensitive health data. This study evaluates synthetic tabular data based on fidelity and utility for predictive models. Fidelity is measured through univariate distribution and bivariate differential pairwise correlations, while utility is measured by comparing machine learning model performance trained on synthetic and real data. Results show highly similar model performance on synthetic and real data. We also explore the potential of using synthetic data for hyperparameter tuning. Our findings reveal a strong correlation between prediction accuracy on synthetic and real data, suggesting that hyperparameters optimized using synthetic data can be effectively applied to models trained on real datasets for optimal results.<\/jats:p>","DOI":"10.3233\/shti250398","type":"book-chapter","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T08:55:50Z","timestamp":1747385750000},"source":"Crossref","is-referenced-by-count":1,"title":["How Useful Is Synthetic Data in Developing Predictive Models for Health?"],"prefix":"10.3233","author":[{"given":"Mohammad Ahmed","family":"Basri","sequence":"first","affiliation":[{"name":"System Design Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9401-7895","authenticated-orcid":false,"given":"Helen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Public Health Sciences and Cheriton School of Computer Science, University of Waterloo, Ontario, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Intelligent Health Systems \u2013 From Technology to Data and Knowledge"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250398","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T08:55:50Z","timestamp":1747385750000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,15]]},"ISBN":["9781643685960"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250398","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,15]]}}}