Building Machine Learning Models to Improve Hypertension Diagnosis

Roman Haynatzki, Thomas Windle, John Windle

Research output: Contribution to journalConference articlepeer-review


Hypertension (HT) is a major risk factor for heart disease, stroke, and kidney disease. A diagnosis of HT is based on two or more blood pressure (BP) readings taken on two or more visits, and such diagnosis may vary depending on the threshold BP values utilized by specific guidelines. Our hypertension identification approach has been recently proposed and validated for HT diagnosis and could be used as ground truth in modeling of latent HT diagnosis. Our approach to modeling of latent HT diagnosis with high precision will leverage analytics to "Big Data", such as electronic health records (EHRs). In this work, we will review the time complexity underlying the classical ML methods XGBoost (XGB) and Artificial Neural Networks (ANN). In particular, we compare the XGB and ANN to leverage their strengths. The performance of all algorithms for diagnosing HT will be characterized using the area under the curve (AUC) approach on a big EHR longitudinal dataset. Predictor variable importance and model interpretability will be assessed using the Shapley values approach.

Original languageEnglish (US)
Article number012001
JournalJournal of Physics: Conference Series
Issue number1
StatePublished - 2023
Event15th Conference of the Euro-American Consortium for Promoting the Application of Mathematics in Technical and Natural Sciences, AMiTaNS 2023 - Hybrid, Albena, Bulgaria
Duration: Jun 21 2023Jun 26 2023

ASJC Scopus subject areas

  • General Physics and Astronomy


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