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O‘pka saratoniga chalinganlar ulushi onkologik kasallik turlari orasida eng katta ko‘rsatkichlarni tashkil etmoqda. Ushbu tur saraton kasalligiga chalingan bemorlar o‘limi onkologik kasalliklar tufayli vafot etayotganlarning 18 foizini tashkil etayotganligi bu borada tadqiqot olib borish zarurligini ko‘rsatmoqda. Mamlakatimizda ham yildan-yilga o‘pka saratoniga chalingan bemorlar hamda mazkur kasallik asoratlaridan vafot etayotganlar soni ortib borayotgani buning yaqqol misolidir. Mazkur maqolada tayanch vektorlar usuli yordamida bemorlardagi o‘pka saratoni kasalligiga chalinish darajasini tasniflash masalasi ko‘rib chiqilgan. O‘quv tanlanma uchun kaggle.com saytidan olingan etalon ma’lumotlardan foydalanilgan. Tadqiqot usuli sifatida tanlangan tayanch vektorlar usulining asosiy bosqichlari atroflicha yoritilgan. Kasallikka chalinish darajalarining tasniflash natijalari jadval va grafiklar asosida keltirilgan. Xulosa o‘rnida tayanch vektorlar usuli nafaqat turli sohalar, balki tibbiyot sohasida ham qo‘llanilishi ijobiy yechim bo‘la olishi isbotlangan. Bundan tashqari, tadqiqotda foydalanilgan o‘quv tanlanma ham real jarayonga tatbiq etishga loyiqligi ta’kidlangan.

  • Ссылка в интернете
  • DOIhttps://dx.doi.org/10.36522/2181-9637-2024-1-1
  • Дата создание в систему UzSCI 07-10-2024
  • Количество прочтений 25
  • Дата публикации 08-02-2024
  • Язык статьиO'zbek
  • Страницы8-20
Русский

Среди онкологических заболеваний рак лёгкого имеет наибольшую долю. Тот факт, что смертность больных этим видом рака составляет 18 % от смертности из всех онкологических заболеваний, показывает актуальность проведения исследований в этом направлении. Убедительным примером этого является и то, что в нашей стране с каждым годом растёт количество больных раком лёгкого, в том числе и умирающих от осложнений этого заболевания. В данной статье рассмотрена задача классификации заболеваний рака лёгкого у пациентов при помощи метода опорных векторов. В качестве обучающей выборки использовались эталонные данные, полученные Kaggle.com. Подробно описаны основные этапы метода опорных векторов, выбранного в качестве метода исследования. Результаты классификации заболеваний поясняются с помощью таблиц и графиков. Было доказано, что метод опорных векторов может быть эффективным решением при применении в медицине. Кроме того, подчёркивается, что использованная в исследовании обучающая выборка вполне применима в реальном процессе.

Ўзбек

O‘pka saratoniga chalinganlar ulushi onkologik kasallik turlari orasida eng katta ko‘rsatkichlarni tashkil etmoqda. Ushbu tur saraton kasalligiga chalingan bemorlar o‘limi onkologik kasalliklar tufayli vafot etayotganlarning 18 foizini tashkil etayotganligi bu borada tadqiqot olib borish zarurligini ko‘rsatmoqda. Mamlakatimizda ham yildan-yilga o‘pka saratoniga chalingan bemorlar hamda mazkur kasallik asoratlaridan vafot etayotganlar soni ortib borayotgani buning yaqqol misolidir. Mazkur maqolada tayanch vektorlar usuli yordamida bemorlardagi o‘pka saratoni kasalligiga chalinish darajasini tasniflash masalasi ko‘rib chiqilgan. O‘quv tanlanma uchun kaggle.com saytidan olingan etalon ma’lumotlardan foydalanilgan. Tadqiqot usuli sifatida tanlangan tayanch vektorlar usulining asosiy bosqichlari atroflicha yoritilgan. Kasallikka chalinish darajalarining tasniflash natijalari jadval va grafiklar asosida keltirilgan. Xulosa o‘rnida tayanch vektorlar usuli nafaqat turli sohalar, balki tibbiyot sohasida ham qo‘llanilishi ijobiy yechim bo‘la olishi isbotlangan. Bundan tashqari, tadqiqotda foydalanilgan o‘quv tanlanma ham real jarayonga tatbiq etishga loyiqligi ta’kidlangan.

English

Among all cancers, lung cancer accounts for the largest proportion of patients. The fact that the mortality rate of patients with this type of cancer makes 18% of the number of deaths from oncologic diseases, shows relevance of the research in this area. A clear evidence of this is the fact that in our country, statistics of lung cancer patients and those who die from its implications, is increasing every year. This paper reviews the issue of classifying the level of lung cancer in patients using the support vector method. The benchmark data obtained from kaggle.com was used as a training set. The main stages of the support vector method chosen as the research method are being closely described. Findings from classification of morbidity levels are being explained using tables and graphs. The study proves that the support vector method can serve as a positive solution for application in various fields including the medical practice. Moreover, it is being emphasized that the training set used in the study is worthy of applying in the real process.

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