BibTex Citation Data :
@article{J.Gauss39188, author = {Riza Sahila and Tatik Widiharih and Iut Tri Utami}, title = {ANALISIS KLASIFIKASI MENGGUNAKAN REGRESI LOGISTIK BINER DAN ALGORITMA NAÏVE BAYES CLASSIFIER PADA PENYAKIT HIPERTENSI}, journal = {Jurnal Gaussian}, volume = {13}, number = {2}, year = {2024}, keywords = {Keywords: Hypertension; Classification; Binary Logistic Regression; Naïve Bayes Classifier; Sensitivity.}, abstract = { Hypertension is a primary cause of cardiovascular disease. Approximately 60% of people with hypertension are in developing countries, including Indonesia. In this analytical study, classification will be carried out to prove the status of hypertensive patients or not hypertensive. The classification method used is Binary Logistic Regression and Naïve Bayes Classifier. Binary Logistic Regression is Logistic Regression with the response variable being binary. Naïve Bayes Classifier namely predicting future opportunities using previous data. The factors used in this study were gender, age, height, and weight. The greatest accuracy of classification results is in the proportion of 90%:10%. The accuracy of the classification produced by Binary Logistic Regression method resulted in a sensitivity of 93.33%. The classification accuracy obtained by the Naïve Bayes Classifier with a sensitivity of 63.64%. This shows that the Binary Logistic Regression method has a better sensitivity value. Keywords: Hypertension, Classification, Binary Logistic Regression, Naïve Bayes Classifier, Sensitivity. }, issn = {2339-2541}, pages = {319--327} doi = {10.14710/j.gauss.13.2.319-327}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/39188} }
Refworks Citation Data :
Hypertension is a primary cause of cardiovascular disease. Approximately 60% of people with hypertension are in developing countries, including Indonesia. In this analytical study, classification will be carried out to prove the status of hypertensive patients or not hypertensive. The classification method used is Binary Logistic Regression and Naïve Bayes Classifier. Binary Logistic Regression is Logistic Regression with the response variable being binary. Naïve Bayes Classifier namely predicting future opportunities using previous data. The factors used in this study were gender, age, height, and weight. The greatest accuracy of classification results is in the proportion of 90%:10%. The accuracy of the classification produced by Binary Logistic Regression method resulted in a sensitivity of 93.33%. The classification accuracy obtained by the Naïve Bayes Classifier with a sensitivity of 63.64%. This shows that the Binary Logistic Regression method has a better sensitivity value.
Keywords: Hypertension, Classification, Binary Logistic Regression, Naïve Bayes Classifier, Sensitivity.
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