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Investigating feature-engineered predictors for systolic blood pressure changes in an mHealth-based disease
Masashi Kanai1,2, Sangjun Park3, Takahiro Miki2
1Institute of Transdisciplinary Sciences for Innovation, Kanazawa University, Kanazawa, Japan.
Summary
Feature engineering in mobile health programs improved early prediction of systolic blood pressure changes. However, overall model performance for blood pressure prediction remained similar with or without these engineered features over time.
Area of Science:
- Cardiovascular disease management
- Digital health and mHealth
Background:
- Mobile health (mHealth) programs offer continuous monitoring for disease management.
- Feature engineering may enhance prediction models using longitudinal data.
Purpose of the Study:
- To evaluate if feature-engineered predictors improve systolic blood pressure (SBP) change prediction in an mHealth program.
- Assessing the impact of feature engineering on SBP change prediction accuracy.
Main Methods:
- Analysis of 2318 participants in a 24-week mHealth program (Mystar).
- Development of ElasticNet regression models with and without feature-engineered variables at multiple time points (weeks 4, 8, 12, 22).
- Primary outcome: Change in morning SBP from baseline.
Main Results:
- Feature engineering increased individual predictor correlation with SBP change in the early phase (week 4).
- Model-level performance was similar with and without feature engineering throughout the 24-week program.
- Prediction accuracy improved over time, reaching a correlation of approximately 0.85 by week 22 for both model types.
Conclusions:
- Feature engineering highlights important early predictors but does not substantially alter overall mHealth model performance for SBP change.
- Further research is needed to validate these findings in diverse clinical settings.
Keywords:
blood pressure changedigital biomarkerdigital hypertensionimplemental hypertensionmHealthmachine learningmorning hypertensionpredictionMore Related Videos
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