Structural evaluation and prediction and diagnosis systems analysis of diabetes complications using data mining and deep learning techniques

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.

کلیدواژه ها:

Diabetes diagnosis ، diabetes prediction ، Machine learning ، data mining ، knowledge discovery ، diabetic retinopathy