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https://doi.org/10.21070/ups.2...
Article . 2023 . Peer-reviewed
License: CC BY
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Stroke Disease Prediction Using Random Forest Method

Prediksi Penyakit Stroke Menggunakan Metode Random Forest
Authors: Aji, Priyo Wahyu Setiyo; Suprianto, Suprianto;

Stroke Disease Prediction Using Random Forest Method

Abstract

Stroke is a cerebrovascular disease or brain injury that blocks blood vessels thereby limiting blood supply to the brain. Currently technology is growing. The medical community is greatly helped by the development of technology. One of them is a program that can be used to detect stroke with artificial intelligence. In this study, the data used came from the Kaggle.com website and the researchers used a machine learning method, namely random forest. Random forest is a combination of mutually independent classification trees that come from the same distribution through a voting process. Several stages were carried out in this study including the preprocessing, processing and evaluation stages. The results of this study are an accuracy of 99%.

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Keywords

machine learning, classification, stroke, random forest

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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