This paper proposes an algorithm for constructing expert systems (ES) in comparison with a physician's decision to determine epilepsy in children. The main characteristics of electroencephalography are defined. A decision tree method (DTM), which provides a therapeutic and diagnostic process, is proposed. A method of medical data analysis is proposed, which makes it possible to determine the type of epilepsy in children. The main provisions of a medical expert system for diagnosing epilepsy, based on the decision tree method, are developed. The specialist performs a visual analysis of the EEG and concludes that the EEG is normal. A brief note is given on the analysis of medical data, which allows to determine the type of epilepsy in children. The aim of this study is to develop expert systems for the diagnosis of epilepsy, as well as to create new methods based on the solution tree. The proposed algorithmic and software for determining epilepsy in children with reliability and efficiency of 92 % determines the type of the disease. In a study of 370 patients, we received 186 patients (the rest are healthy), in which the diagnoses coincided with the doctor's diagnoses. The software was created in the Python language, and a Dataset of patients with their disease parameters was created.

Abstract

This paper proposes an algorithm for constructing expert systems (ES) in comparison with a physician's decision to determine epilepsy in children. The main characteristics of electroencephalography are defined. A decision tree method (DTM), which provides a therapeutic and diagnostic process, is proposed. A method of medical data analysis is proposed, which makes it possible to determine the type of epilepsy in children. The main provisions of a medical expert system for diagnosing epilepsy, based on the decision tree method, are developed. The specialist performs a visual analysis of the EEG and concludes that the EEG is normal. A brief note is given on the analysis of medical data, which allows to determine the type of epilepsy in children. The aim of this study is to develop expert systems for the diagnosis of epilepsy, as well as to create new methods based on the solution tree. The proposed algorithmic and software for determining epilepsy in children with reliability and efficiency of 92 % determines the type of the disease. In a study of 370 patients, we received 186 patients (the rest are healthy), in which the diagnoses coincided with the doctor's diagnoses. The software was created in the Python language, and a Dataset of patients with their disease parameters was created.