Jafari Kheirabadi, Samira

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  داﻧﺷﮔﺎه ﻋﻠوم ﭘزﺷﮐﯽ و ﺧدﻣﺎت ﺑﮫداﺷﺗﯽ درﻣﺎﻧﯽ ﮐرﻣﺎن   ﺑﮫداﺷتداﻧﺷﮐده   ﯾﺳﺗﯽ آﻣﺎر ز ﭘﺎﯾﺎن ﻧﺎﻣﻪ ﻣﻘطﻊ ﮐﺎرﺷﻧﺎﺳﯽ ارﺷد  ﻋﻧوانﺗرﯾن ھﻣﺳﺎﯾﮔﺎن، ﻣﺎﺷﯾن ﺑردار ﭘﺷﺗﯾﺑﺎن و ﻧزدﯾکK ھﺎی ﻣﻘﺎﯾﺳﻪ روشﮐﻣر در زدﮔﯽ دﯾﺳک  ﺑﻧدی اﻓراد دارای ﺑﯾروندر طﺑﻘﻪ رﮔرﺳﯾون ﻟﺟﺳﺗﯾک  ۷۹۳۱اﺳﺗﺎن ﮐرﻣﺎن در ﺳﺎل     ﺗوﺳط   ﺳﻣﯾرا ﺟﻌﻔری ﺧﯾرآﺑﺎدی  اﺳﺗﺎد راھﻧﻣﺎ ﺗﺎﻧﯾﺎ دھشدﮐﺗر ﺧﺎﻧم   اﺳﺎﺗﯾد ﻣﺷﺎور  آﻗﺎی دﮐﺗر ﻓرھﺎد اﯾراﻧﻣﻧش / ﺧﺎﻧم دﮐﺗر ﻣﯾﻧﺎ ﺟﻣﺷﯾدی 89 ﺷﻬﺮﻳﻮرﺳﺎل ﺗﺤﺼﻴﻠﻲ   ﭼﻜﻴﺪه   .ﻛﻨﻨﺪﺳﺎﻟﻴﺎﻧﻪ اﻓﺮاد زﻳﺎدي ﺑﺎ ﻧﺎراﺣﺘﻲ ﻛﻤﺮ درد ﺑﻪ ﻣﺮاﻛﺰ ﺑﻬﺪاﺷﺘﻲ و درﻣﺎﻧﻲ ﻣﺮاﺟﻌﻪ ﻣﻲ ﻣﻘﺪﻣﻪ و اﻫﺪاف:  ﺗﺼﻮﻳﺮﺑﺮداري رزوﻧﺎﻧﺲ  زدﮔﻲ دﻳﺴﻚ ﻛﻤﺮ ﺑﻪ ﻣﺮاﻛﺰ وﺟﻮد ﺑﻴﺮونﺑﺴﻴﺎري از ﻣﻮارد ﺑﺮاي ﺗﺸﺨﻴﺺ  دراﻳﻦ اﻓﺮاد   ﻏﻴﺮ ﺿﺮوري، ﺑﺎر ﻣﺎﻟﻲ زﻳﺎدي ﺑﺮاي ﺑﻴﻤﺎر و ﺳﻴﺴﺘﻢ  IRMﺷﻮﻧﺪ. اﻧﺠﺎم ارﺟﺎع داده ﻣﻲ )IRM( ﻣﻐﻨﺎﻃﻴﺴﻲ   ﺳﺆاﻻت ﺳﺎده ﺑﻨﺪي  ﺻﺤﻴﺢ اﻓﺮاد ﺑﺎ اﺳﺘﻔﺎده ازﺑﻬﺪاﺷﺘﻲ ﺑﻪ ﻫﻤﺮاه دارد. ﻫﺪف از اﻧﺠﺎم اﻳﻦ ﻣﻄﺎﻟﻌﻪ، ﻃﺒﻘﻪ  ، ﻣﺎﺷﻴﻦ 1ﺗﺮﻳﻦ ﻫﻤﺴﺎﻳﮕﺎنﻧﺰدﻳﻚ Kﺑﻨﺪي ﻛﻼس )ﺑﻠﻪ / ﺧﻴﺮ( در ﻣﻮرد ﺷﺮاﻳﻂ ﺑﻴﻤﺎر و ﺑﺮاﺳﺎس ﺳﻪ روش دوﺣﺎﻟﺘﻲ  دﻳﺴﻚ زدﮔﻲ ﺑﻴﺮون دﻳﺴﻚ ﻛﻤﺮ و ﻓﺎﻗﺪ زدﮔﻲ ﺑﻴﺮون ﺑﻪ دو ﮔﺮوه داراي  3و رﮔﺮﺳﻴﻮن ﻟﺠﺴﺘﻴﻚ 2ﺑﺮدار ﭘﺸﺘﻴﺒﺎن  ﺑﺎﺷﺪ.ﻛﻤﺮ ﻣﻲ  ﻫﺎي درﻣﺎﻧﻲ در ﺗﺮﻳﻦ ﻛﻠﻴﻨﻴﻚﻛﻠﻴﻨﻴﻚ ﺑﻌﺜﺖ، ﻳﻜﻲ از اﺻﻠﻲﻫﺎي اﻳﻦ ﻣﻄﺎﻟﻌﻪ در دادهﻫﺎ: ﻣﻮاد و روش  ﻧﻔﺮ ﻣﺮد  741ﻧﻔﺮ زن و  352ﻧﻔﺮ،  004اﻧﺪ ﻛﻪ ﺷﺎﻣﻞ ﻣﺮﻛﺰ اﺳﺘﺎن ﻛﺮﻣﺎن، در ﺟﻨﻮب ﺷﺮﻗﻲ اﻳﺮان ﺟﻤﻊ آوري ﺷﺪه  ﻫﺎي ﻣﻮرد ﺑﺮرﺳﻲ ﺷﺎﻣﻞ: ﺳﻦ، ﺟﻨﺴﻴﺖ، ﺳﺎﺑﻘﻪ ﺿﺮﺑﻪ ﺑﻪ ﻛﻤﺮ، ﺑﺮدﻧﺪ. وﻳﮋﮔﻲﺑﺎﺷﺪ ﻛﻪ از ﻛﻤﺮ درد رﻧﺞ ﻣﻲﻣﻲ  ﺣﺴﻲ، داﺷﺘﻦ اﺧﺘﻼل ﺣﺮﻛﺘﻲ، ﺳﻴﮕﺎرﻛﺸﻴﺪن، ﺳﺎﺑﻘﻪ ﻣﺼﺮف ﻣﻮاد ﻣﺨﺪر، وزن و ﻗﺪ اﻓﺮاد داﺷﺘﻦ اﺧﺘﻼل   زدﮔﻲ دﻳﺴﻚ  . ﭘﻴﺎﻣﺪ ﻣﻮرد ﻧﻈﺮ ﻣﺎ، وﺟﻮد ﺑﻴﺮوناﻧﺪﺑﺎﺷﺪ ﻛﻪ ﺑﺎ ﻧﻈﺮ ﻓﻮق ﺗﺨﺼﺺ ﻣﻐﺰ و اﻋﺼﺎب ﮔﺮدآوري ﺷﺪهﻣﻲ  ﺑﻴﻤﺎران، ﺗﻮﺳﻂ ﭘﺰﺷﻚ ﻓﻮق  IRMﺑﺎﺷﺪ ﻛﻪ ﻳﻚ ﭘﻴﺎﻣﺪ دوﺣﺎﻟﺘﻲ اﺳﺖ ﻛﻪ ﻧﺘﻴﺠﻪ واﻗﻌﻲ آن ﺑﻌﺪ از دﻳﺪن ﻛﻤﺮ ﻣﻲ  ﭽﻨﻴﻦ ﺑﺮاي ﺑﺮرﺳﻲ اﺛﺮ ﺣﺠﻢ ﻤﻫ ﺷﻮد.ﻣﺸﺨﺺ ﻣﻲ زدﮔﻲ زدﮔﻲ/ ﺑﺪون ﺑﻴﺮون رت داراي ﺑﻴﺮونﺗﺨﺼﺺ ﺑﻪ ﺻﻮ  ﺗﺮﻳﻦ در ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﺷﺪه اﺳﺖ ﺗﺎ ﺑﺘﻮان اﺑﺘﺪا ﻣﻨﺎﺳﺐ 002و  051، 001، 05ﻧﻤﻮﻧﻪ، ﭼﻬﺎر ﺣﺠﻢ ﻧﻤﻮﻧﻪ ﻣﺘﻔﺎوت   ﻫﺎي آزﻣﻮندﺳﺖ آورد و ﺳﭙﺲ ﻛﺎراﻳﻲ اﻳﻦ ﻣﺪل را روي دادهﻫﺎي ﻣﺠﻤﻮﻋﻪ آﻣﻮزش ﺑﻪ ﻣﺪل را ﺑﺎ اﺳﺘﻔﺎده از داده     ﺑﺮرﺳﻲ ﻧﻤﻮد.  ﻣﺪل ﻣﺎﺷﻴﻦ ﺑﺮدار  001و  05ﻧﺘﻴﺠﻪ ﺑﺮازش ﺳﻪ ﻣﺪل آﻣﺎري، ﻧﺸﺎن داد ﻛﻪ در ﺣﺠﻢ ﻧﻤﻮﻧﻪ ﻫﺎ: ﻳﺎﻓﺘﻪ  ﺗﺮﻳﻦ ﻧﺰدﻳﻚ Kﺗﺎﻳﻲ ﻣﺪل  002و  051ﭽﻨﻴﻦ درﻧﻤﻮﻧﻪ ﺑﺎﺷﺪ. ﻫﻤﺑﻨﺪي اﻓﺮاد ﻣﻲﺮاي ﻃﺒﻘﻪﻣﺪل ﻣﻨﺎﺳﺒﻲ ﺑ ﭘﺸﺘﻴﺒﺎن  اﻳﻦ ﻣﺪل ﺑﻬﺘﺮﻳﻦ ﻣﻘﺪار را ﻧﺴﺒﺖ ﺑﻪ   ﻣﺮﺑﻮط ﺑﻪ ﻣﻼك ارزﻳﺎﺑﻲ ﭘﻨﺞﺗﺮﻳﻦ ﻣﺪل اﺳﺖ زﻳﺮا ﻫﺮ ﻫﻤﺴﺎﻳﮕﺎن، ﻣﻨﺎﺳﺐ  ﻣﻼك ارزﻳﺎﺑﻲ ﻧﺴﺒﺖ  ﭘﻨﺞدو ﻣﺪل دﻳﮕﺮ دارد. ﻣﺪل رﮔﺮﺳﻴﻮن ﻟﺠﺴﺘﻴﻚ ﻧﻴﺰ در ﺗﻤﺎﻣﻲ ﺣﺠﻢ ﻫﺎي ﻧﻤﻮﻧﻪ و ﺑﺎ ﻫﺮ   ﺑﻪ دو ﻣﺪل دﻳﮕﺮ از ﻛﺎراﻳﻲ ﻛﻤﺘﺮي ﺑﺮﺧﻮردار اﺳﺖ.  ﻣﺎﺷﻴﻦ ﺑﺮدار ﭘﺸﺘﻴﺒﺎن در  ﻣﺪل ﻫﺎ ﻧﺸﺎن دادﻧﺪ ﻛﻪ ﻳﺎﻓﺘﻪﻫﺎي ارزﻳﺎﺑﻲ ﺑﻪ ﻣﻼك ﺑﺎ ﺗﻮﺟﻪ ﮔﻴﺮي:ﻧﺘﻴﺠﻪ  ﺗﺎﻳﻲ ﺑﻬﺘﺮﻳﻦ ﻛﺎراﻳﻲ را  002و  051ﻫﺎي ﻫﻤﺴﺎﻳﮕﺎن در ﻧﻤﻮﻧﻪ ﺗﺮﻳﻦﻧﺰدﻳﻚ Kﺗﺎﻳﻲ و ﻣﺪل  001و  05ﻫﺎي ﻧﻤﻮﻧﻪ  ﮔﻴﺮﻳﻢ ﻛﻪ ﻣﻲﺑﺎﺷﺪ. ﺑﻪ ﻃﻮر ﻛﻠﻲ ﻧﺘﻴﺠﻪ ﻫﺎ ﻣﻲﺗﺮﻳﻦ ﻣﺪل در اﻳﻦ دادهدارﻧﺪ. ﻣﺪل رﮔﺮﺳﻴﻮن ﻟﺠﺴﺘﻴﻚ ﻧﻴﺰ ﺿﻌﻴﻒ  ﺗﺮﻳﻦ ﻫﻤﺴﺎﻳﮕﺎن در ﺣﺠﻢ ﻧﻤﻮﻧﻪ ﻧﺰدﻳﻚ Kﻣﺪل  ﺑﺎ اﻃﻼﻋﺎت ﻛﻢ و  ﻣﺪل ﻣﺎﺷﻴﻦ ﺑﺮدار ﭘﺸﺘﻴﺒﺎن در ﺣﺠﻢ ﻧﻤﻮﻧﻪ ﻛﻢ و  ﻣﻨﺎﺳﺐ  ةﺗﻮاﻧﻨﺪ ﻛﻤﻚ ﻛﻨﻨﺪﻣﻲ ي آﻣﺎريﻫﺎاﻳﻦ روشﺑﺎﺷﻨﺪ. ﺑﻨﺪي اﻓﺮاد ﻣﻲﻫﺎي ﻣﻨﺎﺳﺒﻲ ﺑﺮاي ﻃﺒﻘﻪﺎﻻﺗﺮ، ﻣﺪلﺑ                                                             )NNK( srobhgieN tseraeN-K 1 )sMVS( enihcaM rotceV troppuS 2 )RL( noissergeR citsigoL 3   ﺪﻨﺷﺎﺑ يرﺎﻤﻴﺑ ﺮﺘﻬﺑ ﺺﻴﺨﺸﺗ رد نﺎﻜﺷﺰﭘ ياﺮﺑ  و  زا عﺎﺟرا يروﺮﺿﺮﻴﻏ ﺰﻛاﺮﻣ ﻪﺑ نارﺎﻤﻴﺑMRIسﺮﺗ ،،  و ﻲﻧاﺮﮕﻧ  ﻪﻨﻳﺰﻫ ﻞﻤﺤﺗ ﻦﻴﻨﭽﻤﻫ و رﺎﻤﻴﺑ ياﺮﺑ ﻲﻟﺎﻣ رﺎﺑﻲﻧﺎﻣرد و ﻲﺘﺷاﺪﻬﺑ ﺰﻛاﺮﻣ ياﺮﺑ ﻦﻴﮕﻨﺳ يﺎﻫ ﻲﻣ بﺎﻨﺘﺟا.دﻮﺷ   هژاو :يﺪﻴﻠﻛ يﺎﻫﻪﻘﺒﻃ ،يﺪﻨﺑ Kﻚﻳدﺰﻧ ،ﻚﻴﺘﺴﺠﻟ نﻮﻴﺳﺮﮔر ،نﺎﺒﻴﺘﺸﭘ رادﺮﺑ ﻦﻴﺷﺎﻣ ،نﺎﮕﻳﺎﺴﻤﻫ ﻦﻳﺮﺗ   ،ﻲﮔﮋﻳو ،ﺖﻴﺳﺎﺴﺣ ناﺰﻴﻣ ﺺﺧﺎﺷﻪﻘﺒﻃﺢﻴﺤﺻ يﺪﻨﺑنوﺮﻴﺑ ، ﺮﻤﻛ ﻚﺴﻳد ﻲﮔدز            Abstract Introduction: Every year, people with low back pain (LBP) refer to health centers and  are referred to MRI centers to diagnose lumber disc herniation (LDH) in many cases. Doing  unnecessary MRI involves a lot of financial burden for the patient and the health system. The purpose of this study is to classify the people correctly using simple questions (yes / no) about patient conditions, based on three classification methods, K- nearest neighbors (KNN), support vector machine (SVM) and logistic regression (LR) to two groups having lumber disc herniation and not having lumbar disc herniation. Methods: data was gathered at Besat clinic, one of the most important medical clinics in the center of Kerman province, southeast of Iran which included 252 women and 148 men suffering from LBP. The features of this study include: age, gender, waist injury, sensory disorders, movement disorder, smoking, history of drug use, weight and height which have been collected by the neurologist's opinion. The outcome variable is the presence of lumber disc herniation, which is a binary variable and the result of it was being diagnosed after observing the result of MRI by the neurologist in the form of having lumber disc/not having lumber disc. Results: The result of the fitting of three statistical models showed that support vector machine is a suitable model for classifying individuals in a small sample size (50 and 100).    Also, K-nearest neighbors is the most appropriate model as the sample size increases (150 and 200). Because all four criteria for evaluating this model have the best value compared to the other two models. Logistic regression model is also less efficient than the other two models in all sample sizes with all four evaluation criteria.  Conclusion: In the 50 and 100 samples, support vector machine, and K-nearest neighbors in the 150 and 200 samples have the best performance. Logistic regression model is also the weakest model in this data. In general, we conclude that support vector machine in the small sample size and the K-nearest neighbors in the higher sample size are suitable models for classifying individuals.   ٨۶     Kerman University of Medical Sciences  School of Public Health  In Prtial Fulfillment of the Requirements for the Degree Master of Science  Title:  The Comparison of  K-Nearest Neighbors, Support Vector Machine and Logistic regression in classification of people with lumbar disc herniation in Kerman province, 2018   By:  Samira jafari  Supervisor: Dr. Tania Dehesh  Advisers: Dr. Mina Jamshidi Dr. Farhad Iranmanesh     Year: 2019  ٨۶    

The Comparison of  K-Nearest Neighbors, Support Vector Machine and Logistic regression in classification of people with lumbar disc herniation in Kerman province, 2018

http://eprints.kmu.ac.ir/31720/1/7334.pdf

The Comparison of K-Nearest Neighbors, Support Vector Machine and Logistic regression in classification of people with lumbar disc herniation in Kerman province, 2018

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