کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10321746 660749 2015 37 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Classification of healthcare data using genetic fuzzy logic system and wavelets
ترجمه فارسی عنوان
طبقه بندی داده های مراقبت های بهداشتی با استفاده از سیستم منطق فازی ژنتیکی و موجک
کلمات کلیدی
مدل افزایشی استاندارد فازی الگوریتم ژنتیک، تغییر شکل موج، طبقه بندی اطلاعات بهداشتی، سرطان پستان، بیماری قلبی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Healthcare plays an important role in promoting the general health and well-being of people around the world. The difficulty in healthcare data classification arises from the uncertainty and the high-dimensional nature of the medical data collected. This paper proposes an integration of fuzzy standard additive model (SAM) with genetic algorithm (GA), called GSAM, to deal with uncertainty and computational challenges. GSAM learning process comprises three continual steps: rule initialization by unsupervised learning using the adaptive vector quantization clustering, evolutionary rule optimization by GA and parameter tuning by the gradient descent supervised learning. Wavelet transformation is employed to extract discriminative features for high-dimensional datasets. GSAM becomes highly capable when deployed with small number of wavelet features as its computational burden is remarkably reduced. The proposed method is evaluated using two frequently-used medical datasets: the Wisconsin breast cancer and Cleveland heart disease from the UCI Repository for machine learning. Experiments are organized with a five-fold cross validation and performance of classification techniques are measured by a number of important metrics: accuracy, F-measure, mutual information and area under the receiver operating characteristic curve. Results demonstrate the superiority of the GSAM compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus helpful as a decision support system for medical practitioners in the healthcare practice.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 42, Issue 4, March 2015, Pages 2184-2197
نویسندگان
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