Abstract
Asian Americans are the country's fastest-growing racial group, and several studies have focused on the health outcomes of Asian Americans, including perceived health status. Perceived health status provides a summarized view of the health of populations for diverse domains, such as the psychological, social, and behavioral aspects. Given its multifaceted nature, perceived health status should be carefully approached when examining any variables' influence because it results from interactions among many variables. A data-driven approach using machine learning provides an effective way to discover new insights when there are complex interactions among multiple variables. To date, there are not many studies available that use machine learning to examine the effects of diverse variables on the perceived health status of Chinese and Korean Americans. This study aims to develop and evaluate three prediction models using logistic regression, random forest, and support vector machines to find the predictors of perceived health status among Chinese and Korean Americans from survey data. The prediction models identified specific predictors of perceived health status. These predictors can be utilized when planning for effective interventions for the better health outcomes of Chinese and Korean Americans.