فصلنامه تخصصی فناوری اطلاعات و ارتباطات

مروری بر روش‌های هوشمند در تشخیص و پیش‌بینی بیمارهای کبد با استفاده از تکنیک‌های یادگیری ماشین و الگوریتم‌های فراابتکاری

نوع مقاله : مقاله مروری

نویسندگان

1 گروه مهندسی کامپیوتر،دانشگاه آزاد اسلامی واحد مهاباد،ایران

2 گروه مهندسی کامپیوتر، دانشگاه ازاد اسلامی واحد مهاباد، دانشکده فنی و مهندسی

3 مربی، گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشکده مهندسی برق و کامپیوتر، دانشگاه آزاد اسلامی واحد مهاباد، مهاباد، ایران

10.22034/apj.2023.708841
چکیده
یکی از مهم‌ترین مشکلات درجهان، افزایش هزینه‌ها در حوزه سلامت است؛ از پژوهش‌های مهم سال‌های اخیر برای کاهش ایـن هزینـه‌ها، پیش‌بینی بیماری‌ها می‌باشد. بیماری‌های کبد یکی از بیماری‌های جدی در جهان است، زیرا کبـد نقـش حیـاتی در بـدن انسـان دارد و هرگونـه اختلال در کبد باعث مشکلات جدی و جبران ناپذیری در بـدن می‌شـود. اغلب بیماری‌های کبدی تا مراحل پیشرفته، علایم خاصی را نشان نمی‌دهند. نداشتن علایم در مراحل اولیه ممکن است موجب تشـخیص نادرسـت بیمـاری توسـط بسـیاری از پزشـکان گردد که این تشخیص نادرست می‌تواند منجر به درمان اشتباه و تجویز داروی نامناسب و در نتیجه ایجـاد عـوارض حـاد و بلند مدت این بیماری و یا مشکلات دیگر گردد. بنابراین تشخیص زودتر و دقیق‌تر مشـکلات کبـدی بـه کمک تجزیـه و تحلیل دقیق ویژگی‌های مؤثر یک سیستم تشخیص پزشکی اتوماتیک، جهت درمان صـحیح و پیشـگیری از آسـیب‌هـای جدی به این عضو حیاتی، ضروری به نظر می‌رسد. به همین منظور استفاده از تکنیک‌های داده‌کاوی، یـادگیری ماشـین و بهره‌گیری از الگوریتم‌های فراابتکاری جهت ارائه مدلی هوشمند برای تشـخیص زودهنگـام ایـن بیمـاری لازم و ضـروری می‌باشد. بر این اساس هدف این پژوهش بررسی جامع بر بیمار‌های کبدی، روش‌های تشخیص و پیش‌بینی این بیماری‌ها توسط تکنیک‌های یادگیری ماشین و الگوریتم‌های فراابتکاری از نظر اهداف، محدودیت ها و قابلیت‌ها در حوزه پزشکی می‌باشد. نتایج گویا این است الگوریتم جنگل تصادفی، شـبکه‌هـای فـازی ـ عصـبی و الگوریتم بردار پشتیبان نسبت به الگوریتم‌هـای فراابتکاری راه-حل‌های تقریبی را سریع‌تر پیدا می‌کنند و همچنین در مقایسه با الگوریتم‌های قطعی معمولا نتایج بهتری را ارائه می-دهند، همچنین از میان مجموعه داد‌های بیمارهای کبدی مجموعه داده ILPD به دلیل دسترسی آسان‌تر و ابزار MATLAB به دلیل سادگی و قابل فهم بودن بیشترین کاربرد را در تشخیص و پیش‌بینی بیماری‌های کبد دارند

کلیدواژه‌ها


عنوان مقاله English

A review of smart methods in diagnosing and predicting liver diseases using machine learning techniques and meta-heuristic algorithms

نویسندگان English

Wahab Aminiazar 1
Rasoul Farahi 2
Eqbal Khancheh Sepehrardin 3
1 Department of Computer Engineering, Islamic Azad University, Mahabad Branch, Iran
2 Department of Computer Engineering, Islamic Azad University, Mahabad Branch, Technical and Engineering Faculty
3 nstructor, Department of Computer Engineering and Information Technology, Faculty of Electrical and Computer Engineering, Islamic Azad University, Mahabad Branch, Mahabad, Iran
چکیده English

One of the most important problems in the world is the increase in costs in the field of health. One of the important research in recent years to reduce these costs is the prediction of diseases. Liver diseases are one of the most serious diseases in the world, because the liver plays a vital role in the human body, and any liver disorder causes serious and irreparable problems in the body. Most liver diseases do not show specific symptoms until advanced stages. Not having symptoms in the early stages may lead to the wrong diagnosis of the disease by many doctors, and this wrong diagnosis can lead to the wrong treatment and prescription of inappropriate medicine, and as a result, the creation of acute and long-term symptoms of this disease or other problems. Therefore, earlier and more accurate diagnosis of liver problems with the help of detailed analysis of the effective features of an automatic medical diagnosis system, for correct treatment and prevention of serious damage to this vital organ, seems necessary. For this purpose, it is necessary to use data mining techniques, machine learning and innovative algorithms to provide an intelligent model for early diagnosis of this disease. Therefore, the aim of this research is to comprehensively investigate liver diseases, methods of diagnosis and prediction of these diseases by machine learning techniques and meta-heuristic algorithms in terms of goals, limitations and capabilities in the field of medicine. The results show that random forest algorithm, fuzzy neural networks and support vector machine find approximate solutions faster than meta-initiative algorithms, and also usually get better results compared to deterministic algorithms. Also, among the datasets of liver diseases, the ILPD dataset is the most widely used in the diagnosis and prediction of liver diseases due to its easier access and the MATLAB tool due to its simplicity and comprehensibility.

کلیدواژه‌ها English

iver diseases
intelligent diagnosis and prediction
data mining
machine learning techniques
metaheuristic algorithms
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