Predicting Hypertension with Add Health Dataset using Machine Learning Models

Citation

Fan, Zihan (2024). Predicting Hypertension with Add Health Dataset using Machine Learning Models.

Abstract

High blood pressure is a prevalent health concern worldwide, and identifying the factors that
contribute to its development is crucial for prevention and management strategies. This
study aimed to investigate the influence of sex, hereditary factors, habitats, and BMI on
the risk of high blood pressure using machine learning techniques. Several models, including
Logistic Regression, Decision Trees, Random Forests, XGBoost, Support Vector Machines,
and Neural Networks are employed on the public-use sample from the Add Health dataset.

Reference Type

Thesis/Dissertation

Author(s)

Fan, Zihan

Series Author(s)

Wu, Yingnian

Year Published

2024

Volume Number

Masters of Applied Statistics and Data Science

Publisher

University of California - Los Angeles

Reference ID

10449