Tinjauan Komparatif Klasifikasi Penyakit Stroke Berdasarkan Fitur Medis Menggunakan Random Forest

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

Authors

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

Keywords:

Stroke, Random Forest, Classification, Medical Features, Machine Learning

Abstract

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.

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