Prediction of Hypertension Disease Using Decision Tree and Random Forest Methods
Prediksi Penyakit Hipertensi Menggunakan Metode Decision Tree dan Random Forest
DOI:
https://doi.org/10.21070/ups.3200Keywords:
Classification, Hypertension Disease, Decision Tree, Random ForestAbstract
Hypertension is a significant public health issue, often lacking obvious symptoms in patients' functional health. Hypertension stands as a major risk factor for coronary heart disease, heart failure, and stroke. Factors contributing to hypertension include age, gender, family history, genetics, smoking habits, obesity, lack of physical activity, stress, and estrogen use. Presently, technology continues to advance, bringing convenience to the medical community through technological solutions. Among these is a program utilizing artificial intelligence for hypertension detection, implementing machine learning. This research aims to contribute to the development of applications that aid medical professionals, particularly doctors and hospitals, in diagnosing hypertension with optimal accuracy. The methods used in this study are decision tree and random forest. The stages range from reprocessing data to evaluation. The decision tree method achieves an accuracy of 100%, and the same result is obtained from the random forest method.
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