NEURONET DIAGNOSIS AND ONSET AGE PROGNOSIS OF PANCREATITIS BASED ON AN ANALYSIS OF RISK FACTORS

Abstract

Background. In the social and hygienic study, to develop an artificial neural network designed to diagnose pancreatitis and to predict the time of its onset based on an analysis of information about risk factors, and to test the program in clinical practice. Materials and methods. The study was conducted on the materials of 488 patients (including 167 clients with pancreatitis) who underwent inpatient treatment in the city of Kursk for hepatopancreatoduodenal zone diseases. Data processing of information on health risk factors (sex, age, bad habits, stress, professional and family history, previous treatment) was carried out using an internally developed software package – “System of Intellectual Analysis and Diagnosis of Diseases” (Certificate for State Registration No. 2017613090). Results. A new approach to the diagnosis and prediction of pancreatitis based on a neural network analysis of data on risk factors was proposed. The sensitivity and specificity levels of this method equaled to 76.74% (m = 4.16) and 90% (m = 2.96), respectively. The error in predicting the age of probable hospitalization did not exceed 2.87 and 3.02 years (for p = 0.95 and p = 0.99, respectively). At the same time, the system demonstrated additional advantages: non-invasiveness, low requirements for equipment and professional training of health workers, an opportunity to evaluate the result from the time of the onset of the disease. Conclusion. The effectiveness of the proposed approach was confirmed at the stage of clinical approbation with sensitivity and specificity levels corresponding to similar indicators of traditional diagnostic methods – ultrasound, computed tomography and determination of α-amylase and lipase levels.

Keywords

• искусственная нейронная сеть
• многослойный персептрон
• диагностика
• панкреатит
• искусственный интеллект
• прогнозирование.