Prediction of postoperative complications of pediatric cataract patients using data mining
Autor: | Xiaojing Zhou, Shuai Wang, Lin Liu, Liming Wang, Wangting Li, Kai Zhang, Jiewei Jiang, Xiyang Liu |
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Rok vydání: | 2019 |
Předmět: |
Male
0301 basic medicine Pediatrics medicine.medical_specialty Apriori algorithm Association rule learning Clinical Decision-Making lcsh:Medicine Feature selection Cataract Extraction General Biochemistry Genetics and Molecular Biology 03 medical and health sciences Naive Bayes classifier Postoperative Complications 0302 clinical medicine Naïve Bayesian Cataracts medicine Data Mining Humans Child business.industry Research lcsh:R Infant Bayes Theorem General Medicine Prognosis medicine.disease Random forest Medical decision making system Genetic feature selection 030104 developmental biology ROC Curve Binary classification Area Under Curve Child Preschool 030220 oncology & carcinogenesis Association rules mining Female Pediatric cataract business Algorithms |
Zdroj: | Journal of Translational Medicine Journal of Translational Medicine, Vol 17, Iss 1, Pp 1-10 (2019) |
ISSN: | 1479-5876 |
Popis: | Background The common treatment for pediatric cataracts is to replace the cloudy lens with an artificial one. However, patients may suffer complications (severe lens proliferation into the visual axis and abnormal high intraocular pressure; SLPVA and AHIP) within 1 year after surgery and factors causing these complications are unknown. Methods Apriori algorithm is employed to find association rules related to complications. We use random forest (RF) and Naïve Bayesian (NB) to predict the complications with datasets preprocessed by SMOTE (synthetic minority oversampling technique). Genetic feature selection is exploited to find real features related to complications. Results Average classification accuracies in three binary classification problems are over 75%. Second, the relationship between the classification performance and the number of random forest tree is studied. Results show except for gender and age at surgery (AS); other attributes are related to complications. Except for the secondary IOL placement, operation mode, AS and area of cataracts; other attributes are related to SLPVA. Except for the gender, operation mode, and laterality; other attributes are related to the AHIP. Next, the association rules related to the complications are mined out. Then additional 50 data were used to test the performance of RF and NB, both of then obtained the accuracies of over 65% for three classification problems. Finally, we developed a webserver to assist doctors. Conclusions The postoperative complications of pediatric cataracts patients can be predicted. Then the factors related to the complications are found. Finally, the association rules that is about the complications can provide reference to doctors. Electronic supplementary material The online version of this article (10.1186/s12967-018-1758-2) contains supplementary material, which is available to authorized users. |
Databáze: | OpenAIRE |
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