S cientific supervisor’s review of the graduate qualification work “ Design and development of an automated medical diagnostic system (demonstrated on
the diagnosis of Parkinson's disease) ” by Yurevich Vladislav.
The graduation qualification work (GQW) of Yurevich Vladislav Sergeevich is about the issue of automation of medical diagnostics. Research in the field of machine learning, related medicine, the development of programs and algorithms that promote health development, are relevant.
In particular, survey of Parkinson's disease, conducted with automated systems, can bring a positive effect in the real sector of medical care. This disease is incurable and difficult to diagnose at an early stage.
One way to diagnose the disease at an early stage is to analyze the voice and speech of patients with suspected Parkinson's disease. The mathematical aspect of the problem lies in the fact that the implementation of such systems can be based on algorithms of machine learning.
The paper describes the 26 main voice characteristics of the disease, revealed by analyzing the patient's voice records, such as minimum and maximum pitch, the degree of speech rupture, and a number of others.
The support vector method, the decision tree, the nearest neighbor method k and the multilayer neural network were taken as the studied algorithms of machine learning. the cross validation method was used to check the effectiveness of the algorithm.
Experiments showed the effectiveness of a multilayer neural network.
A web application has been developed. It has a set of functions for a laboratory technician who takes analyzes from patients.
A report was made on the substantive part of the work at the XLVIII conference of postgraduate students and students "Control Processes and Stability" (CPS'17).
All tasks have been fulfilled.
I believe that the graduation qualification work of the bachelor Yurevich Vladislav Sergeevich " Design and development of an automated medical diagnostic system (demonstrated on the diagnosis of Parkinson's disease)" deserves an "excellent" score.
Scientific supervisor,
Doctor of Technical Sciences Pechnikov Andrey
Anatolyevich
