Application of Decisions' Roots for Data Analysis on Example of Dataset with Magnetic Susceptibility Values of the Brain Veins and the Alzheimer's Disease

Kozhemyakin, Leonid; Alekseev, Alexander; Никитин, В. Н.

doi:10.1109/summa57301.2022.9973977

2022 4th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA) 2022

DOI: 10.1109/summa57301.2022.9973977

|View full text |Cite

Application of Decisions' Roots for Data Analysis on Example of Dataset with Magnetic Susceptibility Values of the Brain Veins and the Alzheimer's Disease

Leonid Kozhemyakin

Alexander Alekseev

В. Н. Никитин

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Multi-User Digital Platform for Data Mining, Decisions’ Roots Design and Decisions’ Root-Based Neural Networks Training - D2D.Platform

Alekseev

2024

JDS

View full text Add to dashboard Cite

The multi-user digital platform is described, primarily performed for data mining and decision-making support. The discussed platform was named as "Data to Decisions" or shortly D2D.Platform. In spite of the same name discussed platform is differed from the "Data to Decisions Portal and Dashboard" of the U.S. General Services Administration. The main purpose of the suggested digital platform is development of decisions’ roots – graphical tool for decision making support, and training of decisions' root-based neural networks – new type of incompletely connected neural networks. Besides data mining function of the D2D.Platform, the last one is also a no-code integrated development environment and a database management system.

show abstract

Multi-User Digital Platform for Data Mining, Decisions’ Roots Design and Decisions’ Root-Based Neural Networks Training - D2D.Platform

Alekseev

2024

JDS

View full text Add to dashboard Cite

show abstract

Data Preprocessing and Neural Network Architecture Selection Algorithms in Cases of Limited Training Sets—On an Example of Diagnosing Alzheimer’s Disease

et al. 2023

View full text Add to dashboard Cite

This paper aimed to increase accuracy of an Alzheimer’s disease diagnosing function that was obtained in a previous study devoted to application of decision roots to the diagnosis of Alzheimer’s disease. The obtained decision root is a discrete switching function of several variables applicated to aggregation of a few indicators to one integrated assessment presents as a superposition of few functions of two variables. Magnetic susceptibility values of the basal veins and veins of the thalamus were used as indicators. Two categories of patients were used as function values. To increase accuracy, the idea of using artificial neural networks was suggested, but a feature of medical data is its limitation. Therefore, neural networks based on limited training datasets may be inefficient. The solution to this problem is proposed to preprocess initial datasets to determine the parameters of the neural networks based on decisions’ roots, because it is known that any can be represented in the incompletely connected neural network form with a cascade structure. There are no publicly available specialized software products allowing the user to set the complex structure of a neural network, which is why the number of synaptic coefficients of an incompletely connected neural network has been determined. This made it possible to predefine fully connected neural networks, comparable in terms of the number of unknown parameters. Acceptable accuracy was obtained in cases of one-layer and two-layer fully connected neural networks trained on limited training sets on an example of diagnosing Alzheimer’s disease. Thus, the scientific hypothesis on preprocessing initial datasets and neural network architecture selection using special methods and algorithms was confirmed.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Application of Decisions' Roots for Data Analysis on Example of Dataset with Magnetic Susceptibility Values of the Brain Veins and the Alzheimer's Disease

Cited by 2 publications

References 7 publications

Multi-User Digital Platform for Data Mining, Decisions’ Roots Design and Decisions’ Root-Based Neural Networks Training - D2D.Platform

Multi-User Digital Platform for Data Mining, Decisions’ Roots Design and Decisions’ Root-Based Neural Networks Training - D2D.Platform

Data Preprocessing and Neural Network Architecture Selection Algorithms in Cases of Limited Training Sets—On an Example of Diagnosing Alzheimer’s Disease

Contact Info

Product

Resources

About