The Prediction of Hypertension Risk

Alessandro Massaro, Vito O. M. Giardinelli, Gabriele Cosoli, Nicola Magaletti and Angelo Leogrande

MPRA Paper from University Library of Munich, Germany

Abstract: This article presents an estimation of the hypertension risk based on a dataset on 1007 individuals. The application of a Tobit Model shows that “Hypertension” is positively associated to “Age”, “BMI-Body Mass Index”, and “Heart Rate”. The data show that the element that has the greatest impact in determining inflation risk is “BMI-Body Mass Index”. An analysis was then carried out using the fuzzy c-Means algorithm optimized with the use of the Silhouette coefficient. The result shows that the optimal number of clusters is 9. A comparison was then made between eight different machine-learning algorithms for predicting the value of the Hypertension Risk. The best performing algorithm is the Gradient Boosted Trees Regression according to the analyzed dataset. The results show that there are 37 individuals who have a predicted hypertension value greater than 0.75, 35 individuals who have a predicted hypertension value between 0.5 and 0.75, while 227 individuals have a hypertension value between 0.0 and 0.5 units.

Keywords: Predictions; Machine Learning Algorithms; Correlation Matrix; Tobit Model; Fuzzy c-Means Clustering. (search for similar items in EconPapers)
JEL-codes: C00 C01 C02 C50 C80 (search for similar items in EconPapers)
Date: 2022-05-30
New Economics Papers: this item is included in nep-big
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