فصلنامه تخصصی فناوری اطلاعات و ارتباطات

پیش‌بینی بیماری‌های قلبی عروقی با استفاده از شبکه عصبی کانولوشنی مبتنی بر اینترنت اشیا

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده فنی، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران

2 دانشکده برق و کامپیوتر، واحد جهرم، دانشگاه آزاد اسلامی

10.22034/apj.2025.725731
چکیده
یکی از مهم‌ترین کاربردهای اینترنت اشیا در حوزه سلامت، نظارت بر وضعیت بیماران از راه دور است. این فناوری به پزشکان امکان می‌دهد که به‌صورت لحظه‌ای وضعیت سلامت بیماران را بررسی کنند و این امر به‌ویژه برای افراد مبتلا یا مستعد به بیماری‌های قلبی بسیار حیاتی است. پیش‌بینی بیماری‌های قلبی عروقی به‌عنوان یک چالش پیچیده شناخته می‌شود که با دقت پایین در مدل‌های موجود روبه‌رو است. در این تحقیق، یک سیستم توصیه‌گر جدید برای پیش‌بینی بیماری‌های قلبی عروقی پیشنهاد شده است که از شبکه عصبی کانولوشنی برای تجزیه و تحلیل داده‌های فیزیولوژیکی بیماران استفاده می‌کند. داده‌های فیزیولوژیکی از بیماران به‌صورت از راه دور از طریق چهار حسگر بیولوژیکی شامل حسگر نوار قلب، حسگر فشار خون، حسگر ضربان قلب و حسگر قند خون جمع‌آوری می‌شوند. این داده‌ها سپس توسط یک کنترل‌کننده آردینو پردازش می‌شوند و مدل شبکه عصبی کانولوشنی برای پیش‌بینی بیماری قلبی عروقی به کار می‌رود، این مدل با قابلیت‌های برجسته در استخراج ویژگی‌های محلی و بدون نیاز به تحلیل پیچیده توالی زمانی، می‌تواند به‌طور موثری از داده‌های عددی ثابت مانند فشار خون، ضربان قلب، و قند خون برای تشخیص بیماری‌های قلبی استفاده کند. نتایج آزمایش‌ها نشان داد شبکه عصبی کانولوشنی توانسته است ویژگی‌های محلی و غیرزمانی داده‌ها را به‌طور مؤثری استخراج کرده و به مدل کمک کند تا دقت پیش‌بینی را به 98.90% برساند.

کلیدواژه‌ها


عنوان مقاله English

Prediction of Cardiovascular Diseases Using Convolutional Neural Network Based on Internet of Things

نویسندگان English

Seyedeh Fatemeh Abdollahi 1
Seyed Ebrahim Dashti 2
1 Faculty of Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
2 Faculty of Electrical and Computer Engineering, Jahrom Branch, Islamic Azad University
چکیده English

One of the most important applications of the Internet of Things in the field of health is remote monitoring of patients. This technology allows doctors to check the health status of patients in real time, which is especially vital for people suffering from or prone to heart diseases. Prediction of cardiovascular diseases is known to be a complex challenge that faces low accuracy in existing models. In this research, a new recommender system for predicting cardiovascular diseases is proposed that uses a convolutional neural network to analyze physiological data of patients. Physiological data from patients are collected remotely through four biological sensors including ECG sensor, blood pressure sensor, heart rate sensor and blood sugar sensor. These data are then processed by an Arduino controller and the convolutional neural network model is used to predict cardiovascular disease. With outstanding capabilities in extracting local features and without the need for complex time sequence analysis, this model can effectively use fixed numerical data such as blood pressure, heart rate, and blood sugar to diagnose heart diseases. The experimental results showed that the convolutional neural network was able to effectively extract local and non-temporal features of the data and help the model achieve a prediction accuracy of 98.90%.

کلیدواژه‌ها English

Prediction Cardiovascular Disease
Convolutional Neural Network
Internet of Things
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