Tinjauan Komparatif Klasifikasi Penyakit Stroke Berdasarkan Fitur Medis Menggunakan Random Forest

Anggarini Puspita Rarasati
Deytizsa Putri Nur Azizah
Puput Arie Sugiyanto
Arif Suryadin

OKTAL : Jurnal Ilmu Komputer dan Sains
Vol 3 No 09 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains

Stroke is a leading cause of disability and mortality, requiring early prediction and diagnosis to improve patient care quality. One widely used algorithm in this classification is Random Forest, known for its advantages in processing complex data and yielding high accuracy. This study aims to conduct a comparative review of Random Forest applications for stroke classification based on medical features. A comparative analysis is performed across several scholarly journals to assess the algorithm’s effectiveness, accuracy, and performance under various parameter settings and data processing techniques. The results of this study are expected to provide insights into the different implementations of Random Forest in stroke classification and identify potential areas for further research to optimize this method.