PERBANDINGAN KINERJA TEKNIK PENYEIMBANG KUMPULAN DATA DALAM MODEL PREDIKSI PENYAKIT STROKE
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Abstract
Stroke is a dangerous neurological disease in which the blood vessels in the brain become blocked or rupture. Accurate stroke predictions can help treat stroke and improve care. Prediction models can be created using machine learning techniques using patient data from hospitals. During the pre-processing stage, data with empty values are deleted, and the balancing techniques used are Random Oversampling (ROS), Random Undersampling (RUS), and Synthetic Minority Oversampling Technique (SMOTE). The machine learning models used in the experiment are Gaussian Naïve Bayes (GNB), Logistic Regression (LR), and K-Nearest Neighbors (K-NN). The Sensitivity of the model is the evaluation method that is valued in this research. Sensitivity score before using the balancing technique is 33% for GNB and LR, and 1% for K-NN. After using the balancing technique, the greatest sensitivity score for GNB and LR was 76%, and for K-NN it was 73%. It can be concluded that dataset balancing techniques play a big role in improving model performance on highly imbalanced datasets, such as the dataset used in this research.
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