Using association rule mining to identify risk factors for early childhood caries
Само за регистроване кориснике
2015
Аутори
Ivančević, VladimirTušek, Ivan
Tušek, Jasmina
Knezević, Marko
Elheshk, Salaheddin
Luković, Ivan
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Background and objective: Early childhood caries (ECC) is a potentially severe disease affecting children all over the world. The available findings are mostly based on a logistic regression model, but data mining, in particular association rule mining, could be used to extract more information from the same data set. Methods: ECC data was collected in a cross-sectional analytical study of the 10% sample of preschool children in the South Backa area (Vojvodina, Serbia). Association rules were extracted from the data by association rule mining. Risk factors were extracted from the highly ranked association rules. Results: Discovered dominant risk factors include male gender, frequent breastfeeding (with other risk factors), high birth order, language, and low body weight at birth. Low health awareness of parents was significantly associated to ECC only in male children. Conclusions: The discovered risk factors are mostly confirmed by the literature, which corroborates the value of the m...ethods.
Кључне речи:
Risk factor / Objective measure of interestingness / Early childhood caries / Data mining / Association rule miningИзвор:
Computer Methods and Programs in Biomedicine, 2015, 122, 2, 175-181Издавач:
- Elsevier Ireland Ltd, Clare
Финансирање / пројекти:
- Интелигентни системи за развој софтверских производа и подршку пословања засновани на моделима (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-44010)
DOI: 10.1016/j.cmpb.2015.07.008
ISSN: 0169-2607
PubMed: 26271408
WoS: 000363824700007
Scopus: 2-s2.0-84944279501
Институција/група
Fakultet organizacionih naukaTY - JOUR AU - Ivančević, Vladimir AU - Tušek, Ivan AU - Tušek, Jasmina AU - Knezević, Marko AU - Elheshk, Salaheddin AU - Luković, Ivan PY - 2015 UR - https://rfos.fon.bg.ac.rs/handle/123456789/1338 AB - Background and objective: Early childhood caries (ECC) is a potentially severe disease affecting children all over the world. The available findings are mostly based on a logistic regression model, but data mining, in particular association rule mining, could be used to extract more information from the same data set. Methods: ECC data was collected in a cross-sectional analytical study of the 10% sample of preschool children in the South Backa area (Vojvodina, Serbia). Association rules were extracted from the data by association rule mining. Risk factors were extracted from the highly ranked association rules. Results: Discovered dominant risk factors include male gender, frequent breastfeeding (with other risk factors), high birth order, language, and low body weight at birth. Low health awareness of parents was significantly associated to ECC only in male children. Conclusions: The discovered risk factors are mostly confirmed by the literature, which corroborates the value of the methods. PB - Elsevier Ireland Ltd, Clare T2 - Computer Methods and Programs in Biomedicine T1 - Using association rule mining to identify risk factors for early childhood caries EP - 181 IS - 2 SP - 175 VL - 122 DO - 10.1016/j.cmpb.2015.07.008 UR - conv_1759 ER -
@article{ author = "Ivančević, Vladimir and Tušek, Ivan and Tušek, Jasmina and Knezević, Marko and Elheshk, Salaheddin and Luković, Ivan", year = "2015", abstract = "Background and objective: Early childhood caries (ECC) is a potentially severe disease affecting children all over the world. The available findings are mostly based on a logistic regression model, but data mining, in particular association rule mining, could be used to extract more information from the same data set. Methods: ECC data was collected in a cross-sectional analytical study of the 10% sample of preschool children in the South Backa area (Vojvodina, Serbia). Association rules were extracted from the data by association rule mining. Risk factors were extracted from the highly ranked association rules. Results: Discovered dominant risk factors include male gender, frequent breastfeeding (with other risk factors), high birth order, language, and low body weight at birth. Low health awareness of parents was significantly associated to ECC only in male children. Conclusions: The discovered risk factors are mostly confirmed by the literature, which corroborates the value of the methods.", publisher = "Elsevier Ireland Ltd, Clare", journal = "Computer Methods and Programs in Biomedicine", title = "Using association rule mining to identify risk factors for early childhood caries", pages = "181-175", number = "2", volume = "122", doi = "10.1016/j.cmpb.2015.07.008", url = "conv_1759" }
Ivančević, V., Tušek, I., Tušek, J., Knezević, M., Elheshk, S.,& Luković, I.. (2015). Using association rule mining to identify risk factors for early childhood caries. in Computer Methods and Programs in Biomedicine Elsevier Ireland Ltd, Clare., 122(2), 175-181. https://doi.org/10.1016/j.cmpb.2015.07.008 conv_1759
Ivančević V, Tušek I, Tušek J, Knezević M, Elheshk S, Luković I. Using association rule mining to identify risk factors for early childhood caries. in Computer Methods and Programs in Biomedicine. 2015;122(2):175-181. doi:10.1016/j.cmpb.2015.07.008 conv_1759 .
Ivančević, Vladimir, Tušek, Ivan, Tušek, Jasmina, Knezević, Marko, Elheshk, Salaheddin, Luković, Ivan, "Using association rule mining to identify risk factors for early childhood caries" in Computer Methods and Programs in Biomedicine, 122, no. 2 (2015):175-181, https://doi.org/10.1016/j.cmpb.2015.07.008 ., conv_1759 .