بررسی و طبقه‌بندی مشخصه‌های کاربردی در تکنیک شبکه‌ی عصبی به منظور تشخیص بیماری‌های ایسکمیک قلبی: مروری سیستماتیک

نوع مقاله : مقاله مروری

نویسندگان

1 دکتری تخصصی انفورماتیک پزشکی، گروه علوم کامپیوتر، مؤسسه‌ی آموزش عالی غیر دولتی فاران، تهران، ایران

2 دانشجوی کارشناسی ارشد، گروه انفورماتیک پزشکی، دانشکده‌ی مدیریت و اطلاع‌رسانی، دانشگاه علوم پزشکی ایران، تهران، ایران

3 مربی ،گروه مدیریت و فن‌آوری اطلاعات سلامت، دانشکده مدیریت و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران

4 دانشجوی پزشکی، دانشکده‌ی پزشکی، دانشگاه علوم پزشکی جندی‌شاپور، اهواز، ایران

چکیده

مقدمه: امروزه، شیوع بیماری‌های ایسکمیک قلبی به عوارض جبران ناپذیری نظیر مرگ بیماران منجر می‌شود. تشخیص دیر هنگام این گونه بیماری‌ها و راه‌کار تهاجمی تشخیصی آن‌ها، سبب شده است محققین به منظور تشخیص به موقع بیماری، نسبت به تهیه‌ی سیستم تصمیم‌یار تشخیصی مبتنی بر تکنیک شبکه‌ی عصبی ضمن به کارگیری حداقل داده‌ها اقدام نمایند. در این راستا، انتخاب حداقل مشخصه‌های مفید برای طراحی ساختار شبکه‌ی عصبی از اهمیت ویژه‌ای برخوردار است و زمینه‌ی دست‌یابی به بیشترین دقت در اخذ نتایج تصمیم را فراهم می‌آورد.روش‌ها: ابتدا مقالات مرتبط با «تشخیص ایسکمیک قلبی با استفاده از شبکه‌های عصبی مصنوعی» از پایگاه داده‌های معتبر و با استفاده از کلید واژه‌های حساس استخراج گردیدند. سپس، تحلیل و طبقه‌بندی محتوا به روش‌های علمی انجام شد.یافته‌ها: طراحی ساختار شبکه‌ی عصبی با استفاده از مشخصه‌های گوناگون قابل اخذ از داده‌های دموگرافیک، تاریخچه‌ی پزشکی، علایم و نشانه‌های بیماری به ویژه آزمایش‌های پاراکلینیک انجام می‌پذیرد. در این بین، مشخصه‌های روش الکتروکاردیوگرام که در گروه آزمایش‌های پاراکلینیک قرار داشتند، به افزایش چشم‌گیر کارایی شبکه‌ی عصبی منجر شدند.نتیجه‌گیری: بهره‌برداری از این گونه سیستم‌های تصمیم‌یار تشخیصی در محیط‌های عملی، به ضریب اطمینان بالای آن‌ها و برخورداری از مقبولیت پزشکان وابسته است. از این رو، لحاظ کردن مواردی نظیر ارتقای میزان بلوغ طراحی ساختار شبکه‌ی عصبی که به انتخاب حداقل مشخصه‌های بهینه وابسته است و ایجاد زیر ساخت‌های لازم جهت ورود داده‌های واقعی، صحیح و در جریان بیماران به این سیستم‌ها راه‌گشا می‌باشند.

کلیدواژه‌ها

عنوان مقاله [English]

Survey and Classification of Functional Characteristics in Neural Network Technique for the Diagnosis of Ischemic Heart Disease: A Systematic Review

نویسندگان [English]

  • Shirin Ayani 1
  • Khadijeh Moulaei 2
  • Maryam Jahanbakhsh 3
  • Reza Moulaei 4
1 PhD in Medical Informatics, Department of Computer Sciences, Faran (Mehr Danesh) Non-governmental Institute of Virtual Higher Education, Tehran, Iran
2 MSc Student, Department of Medical Informatics, School of Health Managment and Information Siences, Iran University of Medical Sciences, Tehran, Iran
3 Instructor, Department of Management and Health Information Technology, School of Management and Medical Information, Isfahan University of Medical Sciences, Isfahan, Iran
4 Student of Medicine, School of Medicine, Jundishapur University of Medical Sciences, Ahvaz, Iran
چکیده [English]

Background: Nowadays, the prevalence of ischemic heart diseases (IHDs) leads to destructive effects such as patient death. Late diagnosis of such diseases as well as their invasive diagnostic approaches made researchers provide a decision support system based on neural network techniques, while using minimum data set for timely diagnosis. In this regard, selecting minimum useful features is significant for designing neural network structure and it paves the way to attain maximum accuracy in obtaining the results.Methods: In this systematic review, valid databases using sensitive keywords were initially searched out to find articles related to "diagnosing the ischemic heart disease using artificial neural networks" and afterwards, scientific methods were used to analyze and classify the content.Findings: Researchers applied various extractable features from demographic data, medical history, signs and symptoms, and paraclinical examinations, to design the neural network structure. Among them, the features obtained from electrocardiographic test, embedded in paraclinical examinations, had led to a remarkable increase of efficiency in neural network.Conclusion: Utilizing such diagnostic decision support systems in practical environments depends on their high confidence coefficient and physicians’ acceptability. Therefore, it can be useful to improve maturity in the design of the neural network structure depending on the choice of the minimum optimal features, and to create required infrastructures to input patients’ real, accurate, and flowing data in these systems.

کلیدواژه‌ها [English]

  • Decision support systems
  • Ischemic Heart Disease
  • Neural Network
  • Electrocardiogram

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مجله دانشکده پزشکی اصفهان
دوره 35، شماره 460 - شماره پیاپی 460
بهمن و اسفند 1396
صفحه 1830-1839

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سابقه مقاله

  • تاریخ دریافت: 10 مرداد 1396
  • تاریخ بازنگری: 11 آبان 1403
  • تاریخ پذیرش: 17 فروردین 1401

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