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Analysis of Disease Data Patterns in the Elderly with Cardiovascular Patients using the Association Rule Method

Journal of Advances in Information Systems and Technology · Vol. 2 No. 2 (2020) · hlm. 53–58

DOI
10.15294/jaist.v2i2.44310
ISSN
2715-999X, 2714-9714
Terbit
2020-10-30
Penerbit
Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Cardiovascular disease is a disease associated with modern behavior patterns. This disease is now attacking developed countries and has threatened countries that are heading towards modernization. Some sources say cardiovascular causes are stress due to work, hypothyroidism, heart rate, chronic kidney, and many more. In general, the reason tends to be due to unhealthy lifestyles such as eating lots of fatty foods, not exercising, etc. In today's times, people tend not to have a healthy lifestyle because of an increasingly modern lifestyle. This causes the cardiovascular disease to increase rapidly and is one of the leading causes of human death in the world. Therefore, it is necessary to analyze the pattern of factors that cause cardiovascular disease to prevent or anticipate cardiovascular disease in today's era. Association rules using the FP-GROWTH algorithm are a method that can perform tracing on historical data to identify data patterns based on previously identified properties. The relationship pattern between data can be searched by looking at the correlation variable between patients with cardiovascular disease. This study found that obesity is a determinant factor for cardiovascular disease; even when you do not consume alcohol and do not smoke, cardiovascular sufferers.

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/sju/index.php/jaist/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en-US">Analysis of Disease Data Patterns in the Elderly with Cardiovascular Patients using the Association Rule Method</dc:title>
	<dc:creator>Mardiansyah, M. Fadil</dc:creator>
	<dc:creator>Pratama, Rizka Nur</dc:creator>
	<dc:description xml:lang="en-US">Cardiovascular disease is a disease associated with modern behavior patterns. This disease is now attacking developed countries and has threatened countries that are heading towards modernization. Some sources say cardiovascular causes are stress due to work, hypothyroidism, heart rate, chronic kidney, and many more. In general, the reason tends to be due to unhealthy lifestyles such as eating lots of fatty foods, not exercising, etc. In today's times, people tend not to have a healthy lifestyle because of an increasingly modern lifestyle. This causes the cardiovascular disease to increase rapidly and is one of the leading causes of human death in the world. Therefore, it is necessary to analyze the pattern of factors that cause cardiovascular disease to prevent or anticipate cardiovascular disease in today's era. Association rules using the FP-GROWTH algorithm are a method that can perform tracing on historical data to identify data patterns based on previously identified properties. The relationship pattern between data can be searched by looking at the correlation variable between patients with cardiovascular disease. This study found that obesity is a determinant factor for cardiovascular disease; even when you do not consume alcohol and do not smoke, cardiovascular sufferers.</dc:description>
	<dc:publisher xml:lang="en-US">Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang</dc:publisher>
	<dc:date>2020-10-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/sju/jaist/article/view/44310</dc:identifier>
	<dc:identifier>10.15294/jaist.v2i2.44310</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Advances in Information Systems and Technology; Vol 2 No 2 (2020): October; 53-58</dc:source>
	<dc:source>2715-999X</dc:source>
	<dc:source>2714-9714</dc:source>
	<dc:source>10.15294/jaist.v2i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/jaist/article/view/44310/18037</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Journal of Advances in Information Systems and Technology</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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