Structural evaluation and prediction and diagnosis systems analysis of diabetes complications using data mining and deep learning techniques
Publish place: 3rd International Conference on Soft Computing
Publish Year: 1398
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CSCG03_064
تاریخ نمایه سازی: 14 فروردین 1399
Abstract:
Mellitus1, the majority of whom are women. According to the World Health Organization, by 2025, this number is expected to reach over 380 million. Diabetes is a disease without urgent treatment. Data mining techniques play an active role in the application of large data in the healthcare sector. Machine learning algorithms and data extraction make it possible to analyze, identify and anticipate the disease, and help doctors make early decisions. The main purpose of data mining techniques in medical systems is to design an automated tool that accurately analyzes medical information and to inform patients and doctors about the severity of the disease and the type of treatment based on symptoms, history of the patient, and the history of treatment. The purpose of this study was to conduct a systematic review of machine learning algorithms, data mining techniques and tools for diabetes research in relation to prediction, diagnosis, and complications of diabetes. Meanwhile, support vector machine2 techniques, deep neural networks, and the combination of some data mining techniques with K-means clustering have been able to provide good detection and predictive results.
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Authors
Yones Kiyani Aliabadi
Ph.D. Student in Artificial Intelligence, Department of Computer Engineering, Faculty of Engineering, Central Tehran Branch, Islamic Azad University Tehran, Iran;
Iman Attarzadeh
Assistant Professor, Department of Computer Engineering, Faculty of Engineering, Central Tehran Branch, Islamic Azad University Tehran, Iran; Iman
Seyedeh Razieh Mahmudi Nezhad Dezfouli
Ph.D. Student in Artificial Intelligence, Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Iran;