Machine learning models trained on synthetic datasets of multiple sample sizes for the use of predicting blood pressure from clinical data in a national dataset

PLOS ONE · 2 authors, 2 centres

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It found no significant difference in model accuracy when trained on synthetic data, real data, or a combination, suggesting synthetic data can be used as a substitute for training algorithms.

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The study used data from the National Diet and Nutrition Survey (NDNS) to evaluate if synthetic datasets could train machine learning models to predict mean arterial blood pressure. After feature selection, four variables (age, sex, weight, height) were used. Three model types (Bayesian linear regression, random forest, neural network) were trained on four different datasets: a real training set (n=2408), two synthetic sets (n=2408 and n=4816), and a combined real-synthetic set (n=4816). All models were tested on the same real test set (n=424). The synthetic datasets showed high fidelity to the real data. Limitations include that the NDNS data is not representative of the general UK population and the study did not assess generalizability or re-identification risk. The findings support the potential of synthetic data as a privacy-preserving tool for training predictive health algorithms.

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other

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research methods

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AI pipeline

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ORIGINAL PAPER

PMC10019654

Machine learning models trained on synthetic datasets of multiple sample sizes for the use of predicting blood pressure from clinical data in a national dataset

PLOS ONE

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ABSTRACT

Introduction The potential for synthetic data to act as a replacement for real data in research has attracted attention in recent months due to the prospect of increasing access to data and overcoming data privacy concerns when sharing data. The field of generative artificial intelligence and synthetic data is still early in its development, with a research gap evidencing that synthetic data can adequately be used to train algorithms that can be used on real data. This study compares the performance of a series machine learning models trained on real data and synthetic data, based on the National Diet and Nutrition Survey (NDNS). Methods Features identified to be potentially of relevance by directed acyclic graphs were isolated from the NDNS dataset and used to construct synthetic datasets and impute missing data. Recursive feature elimination identified only four variables needed to predict mean arterial blood pressure: age, sex, weight and height. Bayesian generalised linear regression, random forest and neural network models were constructed based on these four variables to predict blood pressure. Models were trained on the real data training set (n = 2408), a synthetic data training set (n = 2408) and larger synthetic data training set (n = 4816) and a combination of the real and synthetic data training set (n = 4816). The same test set (n = 424) was used for each model. Results Synthetic datasets demonstrated a high degree of fidelity with the real dataset. There was no significant difference between the performance of models trained on real, synthetic or combined datasets. Mean average error across all models and all training data ranged from 8.12 To 8.33. This indicates that synthetic data was capable of training equally accurate machine learning models as real data. Discussion Further research is needed on a variety of datasets to confirm the utility of synthetic data to replace the use of potentially identifiable patient data. There is also further urgent research needed into evidencing that synthetic data can truly protect patient privacy against adversarial attempts to re-identify real individuals from the synthetic dataset.

CITATION EXPORT

Anmol Arora, Ananya Arora. Machine learning models trained on synthetic datasets of multiple sample sizes for the use of predicting blood pressure from clinical data in a national dataset. PLOS ONE. (2023). https://doi.org/10.1371/journal.pone.0283094

AUTHORS

2 · 2 CENTRES

Anmol Arora, Ananya Arora

School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
Jeonbuk National University, REPUBLIC OF KOREA

EDITOR / REVIEWER

Sathishkumar V E