In a randomised trial of hypertension incidence prediction models, released in Scientific Reports, researchers found that there was no efficiency discrepancy between machine learning (ML) models and traditional methods, suggesting that ML-based risk forecast models can reliably predict hypertension.Numerous risk prediction models have been created for hypertension, but ML techniques are gaining popularity, necessitating an analysis of their effectiveness and possible therapeutic uses.
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In order to fill a gap in the literature, this study compared the predictive abilities of ML algorithms to those of a traditional Cox proportional hazards (PH) regression model while assessing hypertension risk prediction in a survival environment. All the factors, including the demographics, were taken into consideration.
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