Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Ambidextrous : Journal of Innovation, Efficiency and Technology in Organization
Santinah
Faletehan University
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Predicting the Risk of Hypertension in Adult Patients Using the Random Forest Algorithm Santinah; Dede Brahma Arianto
Ambidextrous Journal of Innovation Efficiency and Technology in Organization Vol. 4 No. 02 (2026): Ambidextrous: Journal of Innovation, Efficiency and Technology in Organization
Publisher : Takaza Innovatix Labs Ltd.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61536/ambidextrous.v4i02.497

Abstract

Hypertension remains a persistent and widespread health problem in the adult population, yet many cases go undetected due to limited early symptoms and reliance on conventional clinical assessment. This study aims to develop and evaluate a hypertension risk prediction model in adult patients using the Random Forest algorithm. This study employed a quantitative approach with an exploratory–predictive study design based on electronic secondary data, with a descriptive–analytical framework utilizing data mining techniques. The study population comprised all adult patients registered at selected healthcare facilities, while the sample consisted of 120 adult patients selected by purposive sampling from the hypertension risk dataset on Kaggle. The instrument used was a structured electronic medical record table, including age, gender, body mass index (BMI), blood pressure, and relevant medical history. The data underwent preprocessing and encoding, then were analyzed using the Random Forest algorithm on the Python platform with the scikitlearn library. Model performance was evaluated using accuracy, precision, recall, and F1score metrics. The results showed that the Random Forest model provided an accuracy of 87.5%, precision of 91.7%, recall of 84.6%, and F1 score of 88.0%, indicating a strong hypertension risk classification capability. The study concluded that Random Forest can be utilized as a reliable decision support system for early detection of hypertension risk in adult populations, especially when integrated with electronic medical records.
Co-Authors Dede Brahma Arianto