Authors : Nne Rena Saturday; Igbudu Ezebunwo Kingsley; Anthony Vivian Onyinyechi

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/8d2dr7ka

DOI : https://doi.org/10.38124/ijisrt/26sep1381

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Amongst the six major constituent of cardiovascular ailment worldwide, hypertension (high blood pressure) is the third in rank. High blood pressure (HBP) serves as a preceding event for many other illnesses, its subtle way of affecting patients makes it important to monitor and find a way of early detection before it becomes a serious illness in the lives of patients. A growing percentage of young adults and teenagers are suffering from hypertension (HBP) in recent times and poses a problem of high mortality rate. This study is focused on predicting the occurrence of HBP in a young adult based on young adults’ responses in order to assist in curbing the problem. A logistic predictive model (binary logistic regression) was used to predict the possibility of a young adult being hypertensive.

Keywords : High Blood Pressure, Young Adults, Machine Learning, Elevated Blood Pressure, Hypertension.

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