Perbandingan Akurasi Algoritma Random Forest Dan Naïve Bayes Dalam Memprediksi Risiko Hipertensi

Suprapti, Tati
Anwar, Saeful

BULLET : Jurnal Multidisiplin Ilmu
Vol. 2 No. 2 (2023): BULLET : Jurnal Multidisiplin Ilmu

Hypertension is one of the leading causes of serious health complications. Therefore, it is crucial to predict hypertension risks early to take preventive measures. This study aims to compare the accuracy of two machine learning algorithms, Random Forest and Naïve Bayes, in predicting hypertension risks using a dataset containing information about factors affecting health. Both algorithms were applied to classify patient data into two categories: high hypertension risk and low hypertension risk. Based on testing using evaluation metrics such as accuracy, precision, recall, and F1-score, the results showed that the Random Forest algorithm performed better than Naïve Bayes, with higher accuracy and more consistent performance. This finding can be used as a reference for the development of a decision support system for early hypertension detection in the community.