Analysis of risk factors for failure of hypertension therapy based on medical history and drug consumption using Random Forest

Desi Irfan; Novica Jolyarni D; Halimah Tusakdiyah Harahap; Baginda Restu Al Ghazali; Riswan Syahputra Damanik

doi:10.55227/ijhet.v2i4.284

Authors

Desi Irfan Information Technology, Faculty of Computer Science, Ika Bina Institute of Technology and Health
Novica Jolyarni D Midwifery, Faculty of Health Sciences, Ika Bina Institute of Technology and Health
Halimah Tusakdiyah Harahap Midwifery, Faculty of Health Sciences, Ika Bina Institute of Technology and Health
Baginda Restu Al Ghazali Information Systems, Faculty of Computers, Ika Bina Institute of Technology and Health
Riswan Syahputra Damanik Information Systems, Faculty of Computers, Ika Bina Institute of Technology and Health

DOI:

https://doi.org/10.55227/ijhet.v2i4.284

Keywords:

Hypertension, Therapy Failure, Random Forest, Machine Learning

Abstract

Cardiovascular disease is a major cause of global morbidity and mortality, with many patients experiencing therapy failure despite treatment. This study analyzes risk factors for failure of antihypertensive therapy based on medical history and drug consumption patterns using the Random Forest algorithm. Retrospective analytical research design using medical record data and structured interviews in hypertensive patients who have undergone treatment for at least one year. The dependent variable was therapy failure, defined as BP ≥140/90 mmHg despite treatment. Independent variables include medical history, drug consumption patterns, and demographic factors. Data is processed by handling missing data, normalization, and feature encoding. The Random Forest model was optimized using GridSearchCV and evaluated based on accuracy, precision, recall and AUC-ROC. Feature importance analysis identifies main risk factors, such as medication adherence, diabetes, and duration of hypertension. The model achieved 86% accuracy (AUC: 0.89), better than logistic regression (accuracy: 78%). These results confirm the importance of patient compliance and comorbidities in hypertension management. This study demonstrates the effectiveness of Random Forest in identifying high-risk patients, with recommendations for prioritization of interventions on medication adherence.

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