پیش‌بینی سکتة مغزی در بیماران همودیالیزی با استفاده از الگوریتم‌های داده‌کاوی

نوع مقاله : مقاله پژوهشی

نویسنده

پژوهشگر پسا دکترا، دانشگاه علوم پزشکی تهران، تهران، ایران

10.22034/dtj.2021.250451.1001

چکیده

«تحلیل داده‌ها» یک مبنای تعیین‌کننده برای تصمیم‌سازی و ایجاد تحول به سمت اهداف مطلوب محسوب می‌شود که یکی از روش‌های مطرح آن داده‌کاوی است. در حوزة سلامت نیز با اجرای داده‌کاوی بر روی بیماران دارای وضعیت حاد «نظیر همودیالیزی‌ها» می‌توان به روندهای آتی آن‌ها تا حدی پی بُرد و به کاهش تشدید عوارضشان کمک کرد. با توجه به اینکه مطالعات سال‌های اخیر حاکی از تشدید ریسک سکتة مغزی در این دسته از بیماران است، هدف از این پژوهش نیز بررسی یک روش مرسوم جراحی در همودیالیزی‌ها و ارتباط آن با بروز سکته است.
روش پژوهش حاضر، استفاده از الگوریتم‌های با ناظر داده‌کاوی و با محوریت «درخت تصمیم» است که از الگوریتم‌های کاربردی در دسته‌بندی متغیرها است و به شناخت عوامل تأثیرگذار کمک می‌کند. داده‌های ورودی شامل 468 بیمار همودیالیزی هستند که در یک دورة پنج ساله مورد مطالعه قرار گرفتند و متشکل از 324 نفر زن و 144 نفر مرد هستند.
این مطالعه نشان داد ریسک ابتلا به سکته مغزی در بیمارانی که جراحی دسترسی عروق آن‌ها با ایجاد کاتتر قبل از فیستول صورت گرفته تا 21/84% بوده درحالی‌که بین سن بیماران دیالیزی با سکته‌شان رابطة معناداری پیدا نشد، اما علاوه بر تعبیة کاتتر، سابقة داشتنِ فشارخون یا دیابت نیز در ابتلا به سکتة مغزی مؤثر بودند.

کلیدواژه‌ها


عنوان مقاله [English]

Predicting Stroke in Hemodialysis Patients Using Data Mining

نویسنده [English]

  • Mohammad Rezapour
Post-Doctoral Researcher, Tehran University of Medical Sciences, Tehran, Iran
چکیده [English]

Data analysis has a fundamental role in decision making process and one effective way to do this is through data mining. In the field of health, by performing data mining on patients with acute conditions such as hemodialysis (HD), their future trends can be partially understood which helps to reduce the severity of their complications. Given that recent studies indicate an increased risk of stroke in HD patients, the aim of this study is to investigate a conventional surgical procedure in hemodialysis and its relationship with stroke.
The method of the present study is the use of supervised data mining algorithms and with the focus on the "decision tree", which is one of the applied algorithms in the classification of variables and helps to identify the influential factors. The input data included 468 of hemodialysis patients who were studied over a five-year period and consisted of 324 females and 144 males.
This study showed that the risk of stroke in patients whose vascular access surgery was performed by catheterization before fistula was 84.21%, while there was no significant relationship between the age of dialysis patients and their stroke, but in addition to catheter implantation, history of blood pressure or diabetes were also linked to stroke.

کلیدواژه‌ها [English]

  • prediction
  • Data mining
  • Stroke
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