Why the study?
Hypertension is a major risk factor for cardiovascular disease, stroke, and kidney failure, requiring models with high sensitivity to identify subjects at risk to prevent its future development.
Do Machine Learning models (Decision tree and Naive Bayes) predict the long-term risk of hypertension in older participants (over 50 years old)?
Do Machine Learning models (Decision tree and Naive Bayes) predict the long-term risk of hypertension in older participants (over 50 years old)?
Machine learning models, specifically Decision tree and Naive Bayes, were developed to predict the long-term risk of hypertension in adults over 50 to facilitate early intervention.
ML models may enable hypertension risk stratification in adults >50; leaves open external validation before clinical use.
Hypertension is a chronic condition characterized by high pressure in the arteries of the human body. As a result, the heart is forced to work more intensively for the normal circulation of blood in the body. It is one of the most important risk factors for future fatal and non-cardiovascular diseases, stroke and kidney failure. In this article, Machine Learning (ML) is used to design effective models for predicting the long-term risk of older participants (over 50 years old) being diagnosed with hypertension. Our purpose is to train models with high sensitivity in identifying subjects at risk to avoid the future development and occurrence of hypertension following the proper interventions. In the context of the adopted methodology, two different class balancing methods are considered, under which features ranking is applied, and two ML models (namely, Decision tree and Naive Bayes) are compared based on Precision, Recall, F-Measure, Accuracy and Area Under Curve (AUC).
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Δρίτσας et al. (2022) studied this question.
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