Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol

Abstract

Purpose: Cardiovascular disease (CVD) is a major worldwide health burden. As the risk factors of CVD, hypertension, and hyperlipidemia are most mentioned. Early stage hypertension in the population with dyslipidemia is an important public health hazard. This study was the application of data-driven machine learning (ML), demonstrating complex relationships between risk factors and outcomes and promising predictive performance with vast amounts of medical data, aimed to investigate the association between dyslipidemia and the incidence of early stage hypertension in a large cohort with normal blood pressure at baseline. Methods: This study analyzed annual health screening data for 71,108 people from 2005 to 2017, including data for 27 risk-related indicators, sourced from the MJ Group, a major health screening center in Taiwan. We used five machine learning (ML) methods—stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), least absolute shrinkage and selection operator regression (Lasso), ridge regression (Ridge), and gradient boosting with categorical features support (CatBoost)—to develop a multi-stage ML algorithm-based prediction scheme and then evaluate important risk factors at the early stage of hypertension, especially for groups with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels within or out of the reference range. Results: Age, body mass index, waist circumference, waist-to-hip ratio, fasting plasma glucose, and C-reactive protein (CRP) were associated with hypertension. The hemoglobin level was also a positive contributor to blood pressure elevation and it appeared among the top three important risk factors in all LDL-C/HDL-C groups; therefore, these variables may be important in affecting blood pressure in the early stage of hypertension. A residual contribution to blood pressure elevation was found in groups with increased LDL-C. This suggests that LDL-C levels are associated with CPR levels, and that the LDL-C level may be an important factor for predicting the development of hypertension. Conclusion: The five prediction models provided similar classifications of risk factors. The results of this study show that an increase in LDL-C is more important than the start of a drop in HDL-C in health screening of sub-healthy adults. The findings of this study should be of value to health awareness raising about hypertension and further discussion and follow-up research.

A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques

“…Interestingly, they identified RBCs, in addition to UP, as an important factor, though they did not elaborate on it. Previous research on UP features supports data on the correlation with RBCs; in fact, some studies show that it may be a risk factor for hypertension [ 48 ].…”

“…Balanced accuracy (BA), sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC), are four well-known metrics [ 46 , 47 , 48 ] utilized to assess the six ML models’ performance. To identify the convincing ML models, the widely used LGR was viewed as the baseline model in this study.…”

Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran

“…We proposed an integrated multi-step ML scheme (Figure 1) to construct a decisiontree model for risk evaluation in patients with NVAF taking different doses of dabigatran. Our protocol applied four ML algorithms: naive Bayes (NB), CART, random forest (RF), and extreme gradient boosting (XGBoost), which have been widely used in various medical informatics applications to select important variables [36][37][38][39][40].…”

Association of atherosclerosis indices, serum uric acid to high‐density lipoprotein cholesterol ratio and triglycerides‐glucose index with hypertension: A gender‐disaggregated analysis

“…Cardiovascular disease (CVD) is an important global health burden 1 . Important risk factors for CVD include hypertension and dyslipidemia 2 . Hypertension is a major preventable cause of CVD and all‐cause death worldwide.…”