Deteksi Dini Penyakit Stroke pada Data Tidak Seimbang Menggunakan SMOTE dan Random Forest
DOI:
https://doi.org/10.70309/ticom.v13i3.156Keywords:
Random Forest, Classification, Ensemble Method, SMOTE, Imbalanced DataAbstract
Loss of blood circulation to the brain causes a stroke, which is also known as a brain attack. In addition, blood clots are also the leading cause of stroke in the brain. Based on the WHO report, stroke is the leading cause of death in Indonesia in 2024, with a death rate reaching 131.8 per 100,000 population. This study aims to classify early detection of stroke disease by applying the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology using the Random Forest algorithm. The data used is public through the website www.kaggle.com, with a total of 4981 records consisting of 11 attributes. The data composition is unbalanced, with 4733 negative stroke data (95%) and 248 positive strokes (5%). Handling imbalanced data using the Synthetic Minority Oversampling Technique (SMOTE). The total data from SMOTE is 5981 records, with 4733 negative data and 1248 positive. After exploring several models, the best model was obtained using Random Forest with the SMOTE approach, producing an accuracy of 80.14%, AUC 0.836, recall 63.33%, and precision 11.42%.
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