Discovering Associative Patterns in Healthcare Data
| العنوان: | Discovering Associative Patterns in Healthcare Data |
|---|---|
| المؤلفون: | Diego de Castro Rodrigues, Ronaldo Martins da Costa, Lucas Prado Osco, Frederico Oliveira, Fabiano Medeiros Tavares, Wilmar Borges Leal Junior, Rommel M. Barbosa, Vilson Soares de Siqueira, Márcio Dias de Lima |
| المصدر: | Proceedings of Sixth International Congress on Information and Communication Technology ISBN: 9789811623769 ICICT (1) |
| بيانات النشر: | Springer Singapore, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Decision support system, Association rule learning, business.industry, Computer science, media_common.quotation_subject, Context (language use), Filter (software), Data science, Knowledge extraction, Health care, Quality (business), business, Associative property, media_common |
| الوصف: | Health care has several knowledge discovery techniques. Among them are association rules, which provide quick access to standards. However, classic algorithms can generate many patterns or fail to identify rare cases relevant to healthcare professionals. This study identified asymmetric associative patterns in health-related data using the Health Association Rules (HAR) algorithm. We use a combined strategy of six metrics to filter, select, and eliminate contradiction steps to find patterns and identify possible rare cases. The proposed solution uses adjustment mechanisms to increase the quality of standards with knowledge of the health professional. The HAR assists health researchers and decision support systems. A survey of 597 studies identified the primary needs and problems of associative patterns in the health context. The HAR identifies characteristics with the highest cause and effect relationship. The experiments were carried out on 13 datasets, where we identified the most pertinent patterns for the datasets without losing relevant knowledge. |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::253b452624d34238d3938c9b42ef485b https://doi.org/10.1007/978-981-16-2377-6_35 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........253b452624d34238d3938c9b42ef485b |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |