دانلود مقاله ISI انگلیسی شماره 46331
عنوان فارسی مقاله

طبقه بندی داده ها مراقبت های بهداشتی با استفاده از سیستم منطق فازی ژنتیکی و ریز موج

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
46331 2015 14 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Classification of healthcare data using genetic fuzzy logic system and wavelets
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 42, Issue 4, March 2015, Pages 2184–2197

کلمات کلیدی
الگوریتم ژنتیک - تبدیل موجک - طبقه بندی داده های بهداشت و درمان - سرطان پستان - بیماری قلبی
پیش نمایش مقاله
پیش نمایش مقاله طبقه بندی داده ها مراقبت های بهداشتی با استفاده از  سیستم منطق فازی ژنتیکی و ریز موج

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.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.