دانلود مقاله ISI انگلیسی شماره 42718
عنوان فارسی مقاله

یک سیستم هوشمند ترکیبی برای طبقه بندی داده های پزشکی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
42718 2014 11 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A hybrid intelligent system for medical data classification
منبع

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

Journal : Expert Systems with Applications, Volume 41, Issue 5, April 2014, Pages 2239–2249

کلمات کلیدی
شبکه های عصبی - طبقه بندی و رگرسیون درخت - جنگل های تصادفی - سیستم های ترکیبی هوشمند - پشتیبانی تصمیم گیری پزشکی
پیش نمایش مقاله
پیش نمایش مقاله یک سیستم هوشمند ترکیبی برای طبقه بندی داده های پزشکی

چکیده انگلیسی

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min–Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.

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