IJE TRANSACTIONS B: Applications Vol. 30, No. 8 (August 2017) 1152-1159   

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H. Hamidi and F. Qaribpour
( Received: May 05, 2017 – Accepted in Revised Form: July 07, 2017 )

Abstract    In this article, a new combined approach of a decision tree and clustering is presented to predict the transmission of genetic diseases. In this article, the performance of these algorithms is compared for more accurate prediction of disease transmission under the same condition and based on a series of measures like the positive predictive value, negative predictive value, accuracy, sensitivity and specificity. The results show that support vector machine algorithm outperformed the other two simple algorithms and the neural network and genetic algorithms offered better prediction at the end, while the proposed combined approach is developed using different parameters and outperformed the simple methods.


Keywords    Genetic disease, Transmitting genetic diseases, K-means clustering algorithm, data mining, decision trees.


چکیده    در این مقاله، به تحلیل و ارزیابی برترین الگوریتم‌های استفاده شده در علم پزشکی برای پیش‌بینی انتقال بیماری‌های ژنتیکی پرداخته شده، و یک روش جدید ترکیبی از درخت تصمیم و خوشه‎بندی ارائه می­شود. در این پژوهش تحت شرایط یکسان بر روی مجموعه داده‌ی استاندارد اعمال شده و بر اساس یکسری معیارهای اندازه‌گیری مانند مقدار پیش‌بینی مثبت، مقدار پیش‌بینی منفی، دقت، حساسیت و ویژگی، به مقایسه‌ی این الگوریتم‌ها برای پیش‌بینی دقیق‌تر انتقال بیماری پرداخته شده‌است. نتایج نشان می‌دهند که الگوریتم ماشین بردار پشتیبان عملکرد بهتری نسبت به دو الگوریتم ساده‌ی دیگر دارد و سپس شبکه عصبی و در آخر الگوریتم ژنتیک پیش‌بینی بهتری انجام داده‌است، درحالی‌که روش پیشنهادی ترکیبی با پارامترهای متفاوت ایجاد شده است؛ در مقایسه با روش‌های ساده عملکرد بهتری دارد.


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