Comparative effectiveness of models and methods for diagnosing diseases of internal organs

Among the various applications of IT in healthcare, automated systems for diagnosing diseases stand out for their utilization of mathematical models in medical decision-making. These systems, capable of analyzing extensive datasets, offer a level of speed and accuracy in diagnosing diseases of internal organs that often surpasses human capability. This paper aims to elucidate and validate the effectiveness of these automated systems through a comprehensive approach involving comparative studies and clinical trials. By examining the accuracy of different mathematical models and highlighting the percentage of cases correctly identified by expert systems, this research underscores the systems' proficiency in rapid data processing and analysis. Drawing on previous research, the complexity of medical diagnostics is explored through the diversity of diagnostic indicators and the development of robust mathematical models based on fuzzy set theory and gradient projection techniques. The paper presents a scheme of the automated diagnostic system, detailing its core components and the innovative use of case-based reasoning. Through a retrospective analysis and clinical trial outcomes, the effectiveness of the proposed system is demonstrated, showcasing its significant potential to enhance diagnostic accuracy and efficiency in the medical field.