Abstract
Currently, large amounts of information is available to clinical specialists ranging from clinical symptoms to various types of biochemical data and results of instrumental methods of diagnostics. In order to optimize decision making and to avoid treatment errors in medical practice, decision support systems based on artificial intelligence methods including artificial neural networks are becoming widely used in diagnostic procedures. Such systems allow increasing the efficiency of clinical analysis due to the processing of complex and interrelated medical data and integrating them into the results of diagnostics carried out by a clinician. This article describes the application of the methodology of artificial neural networks in medical diagnostics on the example of modeling and analyzing the risk of osteoporosis in diabetic patients.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
