چكيده به لاتين
Cardiovascular diseases, which have devastating effects on the structure and function of the heart muscle, are the leading cause of death in the world. The incidence and prevalence of diabetes and obesity has been increasing rapidly in the last century, and related diseases as well as mortality due to obesity and diabetes have created enormous health problems for human societies. One of the most common complications of diabetes is cardiovascular disease, which is increasing the number of people suffering from this group of diseases.
Machine learning is the science by which patterns and hidden relationships between data are discovered. As a subset of artificial intelligence, machine learning algorithms create a mathematical model based on sample data or training data for predicting or making decisions without obvious planning.
In this study, using machine learning tools and data collected from 8162 diabetic patients, 42 different factors including physical factors such as height, weight, waist circumference and also blood factors, including blood sugar level, blood insulin level, terry Blood glycerides and many other blood factors, as well as observations from clinical examinations, including blood pressure, the presence or absence of complications of diabetes in the lower extremities, have been collected at the endocrine center of Imam Khomeini Hospital for about ten years. The various machine learning tools used include simple machine learning methods, neural network-based methods, and hybrid methods.
The main purpose of this study is to create a highly accurate predictive model for predicting cardiovascular disease in patients with diabetes. Finding factors affecting cardiovascular disease caused by diabetes is another goal that has been considered in this study. Finally, cardiovascular diseases were predicted with 82% accuracy. Also, influential factors such as the patient's age, duration of diabetes, hemoglobin A1C1 level, and the presence of diabetes complications in small and large arteries were among the factors that were known to aggravate cardiovascular complications.
