Effective factors in diagnosing the degree of hepatitis C using machine learning
- سال انتشار: 1402
- محل انتشار: مجله پیشگامان انفورماتیک سلامت، دوره: 12، شماره: 1
- کد COI اختصاصی: JR_IJIMI-12-1_018
- زبان مقاله: انگلیسی
- تعداد مشاهده: 230
نویسندگان
Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran
School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia- Dean International, Ajeenkya D Y Patil University, Pune, India- School of Business and Management, Geneva, Switzerland
Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran- Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran
Department of Health Information Technology, Sorkheh School of Allied Medical Sciences, Semnan University of Medical Sciences, Semnan, Iran
چکیده
Introduction: Hepatitis C virus (HCV) is a major public health threat, which can be treated if diagnosed early, but unfortunately, many people with chronic diseases are not diagnosed until the final stages. Machine learning and its techniques can be very helpful in diagnosis. This study examines the factors affecting hepatitis C diagnosis using machine learning. Material and Methods: A total of ۲۷ features were used with a dataset containing ۱۳۸۵ records of patients with different grades of HCV. The dataset was clean and preprocessed to ensure accuracy and consistency. To reduce the dimension of the dataset and determine the effective features three feature selection, Pearson Correlation, ANOVA, and Random Forest, were applied. Among all the algorithms, KNN, random forests, and Deep Neural Networks were selected to be utilized, and then their evaluation metrics, such as Accuracy and Recall. To create prediction models, fifteen features were selected for the mentioned machine learning algorithms. Results: Performance evaluation of these models based on accuracy showed that Deep Learning with Accuracy = ۹۲.۰۶۷ had the highest performance. KNN and Random Forest had almost the same performance after Deep Learning. This performance was achieved on dataset containing features that were selected by ANOVA feature selection. Conclusion: Machine learning has been very effective in solving many challenges in the field of health. This study showed that using data-mining algorithms also can be useful for HCV diagnosing. The proposed model in this study can help physicians diagnose the degree of HCV at an affordable and with high accuracy.کلیدواژه ها
Hepatitis C, Machine Learning, HCVاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.