Komparasi Algoritma Machine Learning dalam Memprediksi Penyakit Gagal Ginjal
DOI:
https://doi.org/10.59059/mutiara.v1i6.781Keywords:
PGGK, K Nearest Neighbor, Support Vector Machine, SMOTEAbstract
Chronic Kidney Disease (CKD) is a serious health problem, with significant impact on patients' quality of life and healthcare costs. In an effort to improve early diagnosis, a comparison was made between several Machine Learning algorithms used for analysis of patient clinical data. This clinical data contains the medical history or health records of patients. The Machine Learning algorithms used in this study include K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression. By searching for the best algorithm through the calculation of Accuracy, Precision, and Recall with comparasion when using SMOTE (Synthetic Minority Oversampling) to balancing the class attribute.
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