Razi Journal of Medical Sciences
مجله علوم پزشکی رازی
RJMS
Medical Sciences
http://rjms.iums.ac.ir
39
journal39
2228-7043
2228-7051
en
jalali
1394
2
1
gregorian
2015
5
1
22
131
online
1
fulltext
fa
مقایسه مدل شبکه عصبی مصنوعی با مدلهای رگرسیونی دادههای شمارشی در پیش بینی تعداد دفعات اهدای خون
Comparison of Artificial neural network model with Count data Regression models for Prediction of blood Donation
آمار زیستی
Biostatistics
پژوهشي
Research
<font face="Times New Roman" size="3"> </font><p class="MsoNormal" style="margin: 0cm 0cm 0pt text-align: justify unicode-bidi: embed direction: rtl text-autospace: text-justify: kashida text-kashida: 0% mso-layout-grid-align: none" dir="rtl"><font size="3"><b><span lang="FA" style="color: black font-family: "B Lotus" mso-bidi-language: FA mso-themecolor: text1">زمینه و هدف:</span></b><span lang="FA" style="color: black font-family: "B Lotus" mso-bidi-language: FA mso-themecolor: text1"> مدلبندی یکی از روشهای مهم برای تبیین رابطه بین متغیر
پاسخ و مستقل میباشد. از آنجا که دادههای مربوط به تعداد دفعات اهدای خون</span><span dir="ltr"></span><span dir="ltr"></span><span lang="FA" style="color: black mso-bidi-language: FA mso-bidi-font-family: "B Lotus" mso-themecolor: text1" dir="ltr"><span dir="ltr"></span><span dir="ltr"></span><font face="Times New Roman"> </font></span><span lang="FA" style="color: black font-family: "B Lotus" mso-bidi-language: FA mso-themecolor: text1">به صورت دادههای شمارشی(گسسته)میباشد،
جهت تبیین آنها</span><span dir="ltr"></span><span dir="ltr"></span><span lang="FA" style="color: black mso-bidi-language: FA mso-bidi-font-family: "B Lotus" mso-themecolor: text1" dir="ltr"><span dir="ltr"></span><span dir="ltr"></span><font face="Times New Roman"> </font></span><span lang="FA" style="color: black font-family: "B Lotus" mso-bidi-language: FA mso-themecolor: text1">مناسبتر است که از توزیعهای متغیرهای گسسته مانند پواسن
یا دوجملهای منفی استفاده کرد. هدف از انجام این مطالعه تحلیل مدلهای شمارشی به
روش شبکه عصبی و مقایسه آن با روشهای آماری کلاسیک و انتخاب بهترین روش برای پیشبینی
تعداد دفعات اهدای خون میباشد. <o:p /></span></font></p><font face="Times New Roman" size="3"> </font><p class="MsoNormal" style="margin: 0cm 0cm 0pt text-align: justify unicode-bidi: embed direction: rtl text-autospace: text-justify: kashida text-kashida: 0% mso-layout-grid-align: none" dir="rtl"><font size="3"><b><span lang="FA" style="color: black font-family: "B Nazanin" mso-bidi-language: FA mso-themecolor: text1">روش کار:</span></b><span lang="FA" style="color: black font-family: "B Nazanin" mso-bidi-language: FA mso-themecolor: text1">در این<span style="mso-spacerun: yes"><font face="B Nazanin"> </font></span>مطالعه از دادههای مربوط به
اهدای خون که در پایگاه انتقال خون شهرکرد جمع آوری شده</span><span dir="ltr"></span><span dir="ltr"></span><span lang="FA" style="color: black mso-bidi-language: FA mso-bidi-font-family: "B Nazanin" mso-themecolor: text1" dir="ltr"><span dir="ltr"></span><span dir="ltr"></span><font face="Times New Roman"> </font></span><span lang="FA" style="color: black font-family: "B Nazanin" mso-bidi-language: FA mso-themecolor: text1">است، استفاده گردید و چهار مدل
رگرسیونی پواسن، دوجمله ای منفی و حالت های صفر انبوه آن ها با روش شبکه عصبی
مصنوعی مورد مقایسه قرار گرفت. برای آموزش شبکه عصبی مصنوعی از الگوریتم آموزشی </span></font><span style="color: black font-size: 10pt mso-bidi-language: FA mso-bidi-font-family: "Times New Roman" mso-ascii-font-family: "Times New Roman" mso-hansi-font-family: "Times New Roman" mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1" dir="ltr"><font face="Times New Roman">BFGS(</font><a href="http://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm"><span style="color: black text-decoration: none mso-bidi-language: AR-SA text-underline: none mso-themecolor: text1"><u><font face="Times New Roman" size="2">Broyden–Fletcher–Goldfarb–Shanno algorithm</font></u></span></a><font face="Times New Roman">)</font></span><span dir="rtl"></span><span dir="rtl"></span><span style="color: black font-family: "B Nazanin" mso-bidi-language: FA mso-themecolor: text1"><span dir="rtl"></span><span dir="rtl"></span><font size="3"> <span lang="FA"><font face="B Nazanin">و برای تعیین مناسب ترین مدل از معیار میانگین
مربعات خطا </font></span></font></span><span dir="ltr"></span><span dir="ltr"></span><span style="color: black font-size: 10pt mso-bidi-language: FA mso-bidi-font-family: "Times New Roman" mso-ascii-font-family: "Times New Roman" mso-hansi-font-family: "Times New Roman" mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1" dir="ltr"><span dir="ltr"></span><span dir="ltr"></span><font face="Times New Roman">(mean-square error)</font></span><span dir="rtl"></span><span dir="rtl"></span><span lang="FA" style="color: black font-size: 10pt mso-bidi-language: FA mso-bidi-font-family: "Times New Roman" mso-ascii-font-family: "Times New Roman" mso-hansi-font-family: "Times New Roman" mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1"><span dir="rtl"></span><span dir="rtl"></span><font face="Times New Roman">(</font></span><font face="Times New Roman"><span style="color: black font-size: 10pt mso-bidi-language: FA mso-bidi-font-family: "Times New Roman" mso-ascii-font-family: "Times New Roman" mso-hansi-font-family: "Times New Roman" mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1" dir="ltr">MSE</span><span dir="rtl"></span><span dir="rtl"></span><span lang="FA" style="color: black font-size: 10pt mso-bidi-language: FA mso-bidi-font-family: "Times New Roman" mso-ascii-font-family: "Times New Roman" mso-hansi-font-family: "Times New Roman" mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1"><span dir="rtl"></span><span dir="rtl"></span>)</span></font><font size="3"><span lang="FA" style="color: black font-family: "B Nazanin" mso-bidi-language: FA mso-themecolor: text1"> استفاده شد. بهترین ساختار شبکه در
داده های آموزش انتخاب و دقت روش شبکه عصبی با بهترین ساختار در داده های آموزش با
مدل های رگرسیونی کلاسیک مورد مقایسه قرار گرفت تا بهترین روش برای پیش بینی تعداد
دفعات مجدد اهدای خون انتخاب گردد.<span style="mso-spacerun: yes"><font face="B Nazanin"> </font></span></span><span style="color: black mso-bidi-language: FA mso-bidi-font-family: Lotus mso-themecolor: text1" dir="ltr"><o:p /></span></font></p><font face="Times New Roman" size="3"> </font><p class="MsoNormal" style="margin: 0cm 0cm 0pt text-align: justify unicode-bidi: embed direction: rtl text-justify: kashida text-kashida: 0%" dir="rtl"><font size="3"><b><span lang="FA" style="color: black font-family: "B Nazanin" mso-bidi-language: FA mso-themecolor: text1">یافتهها:</span></b><span lang="FA" style="color: black font-family: "B Lotus" mso-bidi-language: FA mso-themecolor: text1"> </span><span lang="FA" style="color: black font-family: "B Nazanin" mso-bidi-language: FA mso-themecolor: text1">میزان </span></font><font face="Times New Roman"><span style="color: black font-size: 10pt mso-bidi-language: FA mso-bidi-font-family: "Times New Roman" mso-ascii-font-family: "Times New Roman" mso-hansi-font-family: "Times New Roman" mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1" dir="ltr">MSE</span><span dir="rtl"></span><span dir="rtl"></span></font><span lang="FA" style="color: black font-family: "B Nazanin" mso-bidi-language: FA mso-themecolor: text1"><span dir="rtl"></span><span dir="rtl"></span><font size="3"> برای مدل های رگرسیونی پواسن،
پواسن با صفر انبوه، دوجمله ای منفی و دوجمله ای منفی با صفر انبوه به ترتیب برابر
با 71/2، 54/1، 94/0 و 01/1 و برای روش شبکه عصبی مصنوعی14:17:1 با تابع تبدیل
تانژانت هایپربولیک هم در لایه میانی و هم در لایه خروجی این معیار 056/0 بدست
آمد.<o:p /></font></span></p><font face="Times New Roman" size="3"> </font><p class="MsoNormal" style="margin: 0cm 0cm 0pt text-align: justify unicode-bidi: embed direction: rtl text-justify: kashida text-kashida: 0%" dir="rtl"><font size="3"><b><span lang="FA" style="color: black font-family: "B Nazanin" mso-bidi-language: FA mso-themecolor: text1">نتیجهگیری:</span></b><span lang="FA" style="color: black font-family: "B Lotus" mso-bidi-language: FA mso-themecolor: text1"> </span><span lang="FA" style="color: black font-family: "B Nazanin" mso-bidi-language: FA mso-themecolor: text1">نتایج مطالعه نشان داد که، با توجه به میزان </span></font><font face="Times New Roman"><span style="color: black font-size: 10pt mso-bidi-language: FA mso-bidi-font-family: "Times New Roman" mso-ascii-font-family: "Times New Roman" mso-hansi-font-family: "Times New Roman" mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1" dir="ltr">MSE</span><span dir="rtl"></span><span dir="rtl"></span></font><span lang="FA" style="color: black font-family: "B Nazanin" mso-bidi-language: FA mso-themecolor: text1"><span dir="rtl"></span><span dir="rtl"></span><font size="3">
میتوان روش شبکه عصبی مصنوعی را مناسبترین روش با بالاترین دقت جهت پیشبینی
تعداد دفعات اهدای مجدد خون نسبت به مدل های مورد بررسی در این پژوهش دانست. <o:p /></font></span></p><font face="Times New Roman" size="3"> </font>
<font face="Times New Roman" size="3"> </font><p class="MsoNormal" style="margin: 0cm 0cm 0pt text-align: justify text-justify: kashida text-kashida: 0%"><font size="3"><font face="Times New Roman"><b><span style="color: black mso-themecolor: text1">Background</span></b><b><span style="color: black mso-bidi-font-family: "Times New Roman" mso-ascii-font-family: "Times New Roman" mso-hansi-font-family: "Times New Roman" mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1">:</span></b><span style="color: black mso-bidi-font-family: "Times New Roman" mso-ascii-font-family: "Times New Roman" mso-hansi-font-family: "Times New Roman" mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1"> Modeling is
one of the most important ways for explanation of relationship between
dependent and independent response. Since data, related to number of blood
donations are discrete, to explain them it is better to use discrete variable
distribution like Poison or Negative binomial. This research tries to analyze
numerical methods by using neural network approach and compare it by classic
statistical methods to choose better way to predict the number of blood
donations.<o:p /></span></font></font></p><font face="Times New Roman" size="3"> </font><p class="MsoNormal" style="margin: 0cm 0cm 0pt text-align: justify text-justify: kashida text-kashida: 0%"><font size="3"><font face="Times New Roman"><b><span style="color: black mso-themecolor: text1">Methods</span></b><b><span style="color: black mso-bidi-font-family: "Times New Roman" mso-ascii-font-family: "Times New Roman" mso-hansi-font-family: "Times New Roman" mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1">:</span></b><span style="color: black mso-bidi-font-family: "Times New Roman" mso-ascii-font-family: "Times New Roman" mso-hansi-font-family: "Times New Roman" mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1"> In this
study, data were collected from blood donors at the blood center of the
Sharekord and then four methods were compared by neural network approach. These
methods are: Poisson regression model and its zero inflated, Negative binomial
models and its zero inflated.To learn neural network approach, (BFGS)
Broyden–Fletcher–Goldfarb–Shanno algorithm was used. </span><span style="color: black mso-themecolor: text1">To choose the best model,
mean-square error</span></font><span class="apple-converted-space"><span style="background: white color: black font-family: "Arial","sans-serif" mso-themecolor: text1"> </span></span><span style="color: black mso-themecolor: text1"><font face="Times New Roman">(MSE) was used. The best network structure in teaching data was chosen
and neural network approach resolution was compared by them, to choose the best
approach for prediction the number of blood donations.<o:p /></font></span></font></p><font face="Times New Roman" size="3"> </font><p class="MsoNormal" style="margin: 0cm 0cm 0pt text-align: justify text-justify: kashida text-kashida: 0%"><font size="3"><font face="Times New Roman"><b><span style="color: black mso-bidi-font-family: "Times New Roman" mso-ascii-font-family: "Times New Roman" mso-hansi-font-family: "Times New Roman" mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1">Results:</span></b><span style="color: black mso-bidi-font-family: "Times New Roman" mso-ascii-font-family: "Times New Roman" mso-hansi-font-family: "Times New Roman" mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1"> The MSE for
Poisson regression model, Poisson regression with zero inflated, negative
binomial and negative binomial with zero inflated are respectively 2.71, 1.54,
0.94 and 1.01. For neural network approach 14:17:1 with activation function of
hyperbolic tangent in hidden layer and output layer 0.056 is achieved. <o:p /></span></font></font></p><font size="3"><font face="Times New Roman">
<b><span style="color: black font-family: "Times New Roman","serif" font-size: 12pt mso-bidi-language: AR-SA mso-fareast-font-family: "Times New Roman" mso-ansi-language: EN-US mso-fareast-language: EN-US mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1">Conclusion:</span></b><span style="color: black font-family: "Times New Roman","serif" font-size: 12pt mso-bidi-language: AR-SA mso-fareast-font-family: "Times New Roman" mso-ansi-language: EN-US mso-fareast-language: EN-US mso-ascii-theme-font: major-bidi mso-hansi-theme-font: major-bidi mso-bidi-theme-font: major-bidi mso-themecolor: text1"> </span><span style="color: black font-family: "Times New Roman","serif" font-size: 12pt mso-bidi-language: AR-SA mso-fareast-font-family: "Times New Roman" mso-ansi-language: EN-US mso-fareast-language: EN-US mso-themecolor: text1">The results showed that,
according to amount of MSE, neural network approach is the best method with
highest accuracy to predict the number of blood donations rather than other
methods examined in this article</span></font></font>
رگرسیون پواسن, پواسن با صفر انبوه, دوجمله ای منفی, دوجمله ای منفی با صفر انبوه, شبکه عصبی مصنوعی
Poisson regression, Zero inflated Poisson, Negative binomial, Zero inflated negative binomial, Artificial neural network
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http://rjms.iums.ac.ir/browse.php?a_code=A-10-2011-2&slc_lang=fa&sid=1
shima
Haghani
شیما
حقانی
shima_haghani@yahoo.com
3900319475328460029310
3900319475328460029310
No
Shahrekord University of Medical Sciences, Shahrekord, Iran
دانشگاه علوم پزشکی شهرکرد، شهرکرد، ایران
Morteza
Sedahi
مرتضی
سدهی
sedehi56@gmail.com
3900319475328460029311
3900319475328460029311
Yes
Shahrekord University of Medical Sciences, Shahrekord, Iran
دانشگاه علوم پزشکی شهرکرد، شهرکرد، ایران
Soleiman
Kheiri
سلیمان
خیری
kheiri@hbi.ir
3900319475328460029312
3900319475328460029312
No
Shahrekord University of Medical Sciences, Shahrekord, Iran
دانشگاه علوم پزشکی شهرکرد، شهرکرد، ایران