Biotechnical System of Differential Diagnostics of Serous and Purulent Pyelonephritis in Pregnant Women Based on Fuzzy Logic for Decision-Making.

Urologists and nephrologists attribute pyelonephritis of pregnant women to the category of complicated upper urinary tract infections that threaten the development of a severe purulent-septic process. The frequency of pyelonephritis in pregnant women ranges from 12.2 to 33.8%. In this research, laboratory indicators of the state of immunity and lipid peroxidation using fuzzy decision logic are used to improve the quality of differential diagnosis of serous and purulent pyelonephritis in pregnant women. A space of informative indicators was formed that characterize the state of immune changes, making it possible to carry out the differential diagnosis of pyelonephritis forms in pregnant women with high accuracy. Results of the operation of the obtained decision rules in the control sample showed that the diagnostic efficiency of the proposed method reaches 93%, which is acceptable for use in medical practice.

Prediction of Occupational Diseases Due to Exposure to High Radiation Electromagnetic Environment Using a Fuzzy Logic Model.

Several researchers studied the health impacts of electromagnetic fields in work environment. However, the previous research focuses on the statistical analysis of past exposure. There are no studies that addressed prediction of health symptoms. Prediction and early diagnosis of occupational diseases of electric power workers with acceptable accuracy is needed. The objective of this study is to develop a data driven mathematical model for predicting and diagnosis of occupational diseases in workers in electric power industry. The complex nature of disease occurrence due to electromagnetic radiation is appropriate for the fuzzy rules set by medical experts which are analyzed and validated to produce hybrid fuzzy decision rules. The selected group of medical experts suggested using hormonal disorders, endocrine diseases, coffee abuse, chronic diseases of the internal organs, allergic diseases, cervical osteochondrosis, severe course of infectious diseases, intoxication, injury. The developed hybrid fuzzy logic model predicts high risk of developing nervous system diseases. The prediction accuracy exceeded 0.88, which is acceptable for supporting tool.