کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
387682 660906 2012 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Elucidating clinical context of lymphopenia by nonlinear modelling
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Elucidating clinical context of lymphopenia by nonlinear modelling
چکیده انگلیسی

A nonlinear approach for detecting relative lymphopenia is suggested by using a health data record based on simple clinical parameters. Two classification methods, neural networks and decision trees, were applied to detect whether a patient has a positive or a negative lymphopenia outcome. Due to a large dimension of input space, a feature selection method was used in the pre-processing stage. All tested models were validated on the same out-of-sample dataset, and a 10-fold cross-validation procedure for testing generalization ability of the models was conducted. The models were compared according to their classification accuracy in the sense of the average hit rate, specificity and sensitivity. The results show that (1) the best neural network model slightly outperforms the decision tree model, (2) the reduced model provides even higher accuracy than the models with all available data, and (3) both methods similarly rank five important predictors of lymphopenia. The paper discusses the relevance of extracted features, and suggests some guidelines for further research.


► We model a relative lymphopenia using a systematic health data record.
► Artificial neural networks and decision trees were used to recognize positive patients.
► Neural network model slightly outperformed the decision tree model.
► Five important predictors of lymphopenia were extracted and discussed.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 39, Issue 12, 15 September 2012, Pages 10889–10897
نویسندگان
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