{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T11:43:50Z","timestamp":1767008630549,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:00:00Z","timestamp":1682380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of the Russian Federation"},{"name":"Perm Scientific and Educational Center \u201cRational Subsoil Use\u201d"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper aimed to increase accuracy of an Alzheimer\u2019s disease diagnosing function that was obtained in a previous study devoted to application of decision roots to the diagnosis of Alzheimer\u2019s 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\u2019 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\u2019s disease. Thus, the scientific hypothesis on preprocessing initial datasets and neural network architecture selection using special methods and algorithms was confirmed.<\/jats:p>","DOI":"10.3390\/a16050219","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T04:27:02Z","timestamp":1682396822000},"page":"219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Data Preprocessing and Neural Network Architecture Selection Algorithms in Cases of Limited Training Sets\u2014On an Example of Diagnosing Alzheimer\u2019s Disease"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5033-6694","authenticated-orcid":false,"given":"Aleksandr","family":"Alekseev","sequence":"first","affiliation":[{"name":"Administrative Directorate for Organization of Scientific Researches, Perm National Research Polytechnic University, Perm 614990, Russia"},{"name":"Perm Decision Making Support Center, Perm 614107, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leonid","family":"Kozhemyakin","sequence":"additional","affiliation":[{"name":"Perm Decision Making Support Center, Perm 614107, Russia"},{"name":"Department of Economics and Finance, Perm National Research Polytechnic University, Perm 614990, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladislav","family":"Nikitin","sequence":"additional","affiliation":[{"name":"Perm Decision Making Support Center, Perm 614107, Russia"},{"name":"Department of Computational Mathematics, Mechanics and Biomechanics, Perm National Research Polytechnic University, Perm 614990, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julia","family":"Bolshakova","sequence":"additional","affiliation":[{"name":"Department of Computational Mathematics, Mechanics and Biomechanics, Perm National Research Polytechnic University, Perm 614990, Russia"},{"name":"Diagnosing Systems, Perm 614101, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1080\/23808993.2021.1993595","article-title":"Capabilities of neural network technologies for extracting new medical knowledge and enhancing precise decision making for patients","volume":"7","author":"Yasnitsky","year":"2022","journal-title":"Expert Rev. 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