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.

Kuchkarova, Nozima

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.

dc.contributor.author	Kuchkarova, Nozima
dc.date.accessioned	2022-12-13T06:48:52Z
dc.date.available	2022-12-13T06:48:52Z
dc.date.issued	2022-12-12
dc.identifier.issn	2181-3337
dc.identifier.uri	http://repository.tma.uz/xmlui/handle/1/4932
dc.description.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.	en_US
dc.publisher	METHODOLOGY FOR BUILDING A MEDICAL EXPERT SYSTEM FOR DISEASE DIAGNOSIS	en_US
dc.subject	expert system, electroencephalography, epilepsy, decision tree, method, algorithm, program.	en_US
dc.title	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.	en_US
dc.title.alternative	expert system, electroencephalography, epilepsy, decision tree, method, algorithm, program.	en_US