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Edward Yo
Department of Informatics Engineering, Pelita Indonesia Institute of Business and Technology, Pekanbaru, Riau, Indonesia
Computer Science & IT Engineering

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Journal : data science insights

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Classification of Stroke Diseases Using Decision Tree Edward Yo
Data Science Insights Vol. 4 No. 2 (2026): Journal of Data Science Insights
Publisher : PT. Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v4i2.201

Abstract

Stroke, a very virulent disease and the risk of carrying death and the most common disease experienced by almost everyone. This study aims to find out whether patients have stroke by using various machine learning algorithms to determine the highest classification algorithm. Disease data was taken from a kaggle created by Federico Soriano with attribute data such as gender, age, hypertension, heart_disease, avg_glucose_level, bmi, smoking_status, and stroke. For data modeling, the cross-validaiton and hold out methods are used with decision tree, random forest, k-NN and naive bayes algorithms. The cross-validation and hold out accuracy values on the decision tree algorithm were 95.74% and 95.72%, random forest was 95.74% and 92.87%, k-NN was 92.46% and 92.87%, and naïve bayes were 91.75% and 93.21%. Then it was implemented in python using a decision tree algorithm with an accuracy value of 91% and a simple GUI application was formed to make it easier to train and test data to show the appearance of stroke.
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