Hypertension Prediction Using Optimal Random Forest and Real Medical Data

Abstract

Long-lasting and difficult-to-treat, hypertension frequently leads to serious and life-threatening diseases. As a result, early risk assessment and prevention of hypertension are crucial. The majority of research currently available ignore the preprocessing analysis of real medical data, particularly the analysis of missing values, in favor of using clean data to increase the performance of hypertension prediction. Thus, in this study, real but incomplete data were subjected to preprocessing analysis including missing value analysis and feature divergence analysis, and then a Bayesian optimization technique was employed to find the optimal random forest model. Experimental results showed that proper missing value strategy (i.e., MissForest) can slightly enhance the data quality and produce slightly better predictive performance (from 0.001% to 0.069%) even the missing rate is less than 1%. Additionally, compared to using the original features, removing some features with little divergence can lower the dimensionality and even marginally enhance performance by 0.161% in terms of median AUC across 50 runs. Furthermore, the optimal random forest can demonstrate better hypertension discrimination in real medical data. In our case, the optimal random forest can improve the performance of the non-optimized forest by up to 3.51%.