Pelatihan Deteksi Risiko Hipertensi Dengan Analisis Data Riwayat Medis Berbasis Random Forest Untuk Tenaga Kesehatan Masyarakat

Desi Irfan; Evri Ekadiansyah; Halimah Tusakdiyah Harahap; Novica Jolyarni Dornik; Yusril Iza Mahendra Hasibuan

doi:10.62027/sevaka.v1i4.527

Authors

Desi Irfan Institut Teknologi dan Kesehatan Ika Bina, Rantauprapat, Indonesia
Evri Ekadiansyah Universitas Batuta, Medan, Indonesia
Halimah Tusakdiyah Harahap Institut Teknologi dan Kesehatan Ika Bina, Rantauprapat, Indonesia
Novica Jolyarni Dornik Institut Teknologi dan Kesehatan Ika Bina, Rantauprapat, Indonesia
Yusril Iza Mahendra Hasibuan Institut Teknologi dan Kesehatan Ika Bina, Rantauprapat, Indonesia

DOI:

https://doi.org/10.62027/sevaka.v1i4.527

Keywords:

Hypertension, Random Forest, Machine Learning, Medical History, Public Health

Abstract

Hypertension is one of the most prevalent non-communicable diseases and a major risk factor for heart disease, stroke, and kidney disorders. The high prevalence of hypertension cases in the community, particularly in the working area of Puskesmas Kota Rantau Prapat, highlights the urgent need for more effective early detection efforts to prevent severe complications in the future. However, the limited capacity of healthcare workers in utilizing data analysis technologies has resulted in hypertension risk detection being dominated by conventional methods, which are often less accurate and inefficient. To address this issue, this community service program was conducted through training on the application of the Random Forest algorithm to analyze patients’ medical history data in order to detect hypertension risks. The training method included an introduction to the fundamentals of machine learning, data pre-processing stages, implementation of the Random Forest algorithm, and interpretation of prediction results. The outcomes of the program demonstrated that healthcare workers were able to understand the use of data analysis technologies to support more accurate early detection of hypertension. Furthermore, the participants gained practical skills in utilizing medical datasets to produce predictions that can serve as a decision-support tool for preventive medical actions.Thus, this training contributed to enhancing the capacity of community healthcare workers in integrating machine learning-based technologies into preventive healthcare services. This program is expected to serve as an initial step toward developing more effective, efficient, and sustainable data-driven health systems.

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