کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
414963 681126 2015 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Ordinal Logic Regression: A classifier for discovering combinations of binary markers for ordinal outcomes
ترجمه فارسی عنوان
رگرسیون منطقی مرتبه: یک طبقه بندی برای کشف ترکیبی از نشانگرهای باینری برای نتایج دستورالعمل
کلمات کلیدی
رگرسیون منطقی، واکنش موضعی، درختان تصمیم گیری
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی

In medicine, it is often useful to stratify patients according to disease risk, severity, or response to therapy. Since many diseases arise from complex gene–gene and gene–environment interactions, patient strata may be defined by combinations of genetic and environmental factors. Traditional statistical methods require specifying interactions a priori making it difficult to identify high order interactions. Alternatively, machine learning methods can model complex interactions, however these models are often difficult to interpret in a clinical setting. Logic regression (LR) enables modeling a binary outcome using logical combinations of binary predictors yielding easily interpretable models. However LR, as currently available, cannot model ordinal responses. This paper extends LR to model an ordinal response and the resulting method is called Ordinal Logic Regression (OLR). Several simulations comparing OLR and Classification and Regression Trees (CART) demonstrate that OLR is superior to CART for identifying variable interactions associated with an ordinal response. OLR is applied to data from a study to determine associations between genetic and health factors with severity of adult periodontitis. Ordinal Logic Regression is publicly available on CRAN in the OrdLogReg package, http://cran.r-project.org/.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 82, February 2015, Pages 152–163
نویسندگان
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