Construction of a Compact and High-Precision Classifier in the Inductive Learning Method for Prediction and Diagnostic Problems

Author:

Kuzmich Roman,Stupina Alena,Yasinskiy AndreyORCID,Pokushko MariiaORCID,Tsarev RomanORCID,Boubriak Ivan

Abstract

The study is dictated by the need to make reasonable decisions in the classification of observations, for example, in the problems of medical prediction and diagnostics. Today, as part of the digitalization in healthcare, decision-making by a doctor is carried out using intelligent information systems. The introduction of such systems contributes to the implementation of policies aimed at ensuring sustainable development in the health sector. The paper discusses the method of inductive learning, which can be the algorithmic basis of such systems. In order to build a compact and high-precision classifier for the studied method, it is necessary to obtain a set of informative patterns and to create a method for building a classifier with high generalizing ability from this set of patterns. Three optimization models for the building of informative patterns have been developed, which are based on different concepts. Additionally, two algorithmic procedures have been developed that are used to obtain a compact and high-precision classifier. Experimental studies were carried out on the problems of medical prediction and diagnostics, aimed at finding the best optimization model for the building of informative pattern and at proving the effectiveness of the developed algorithmic procedures.

Funder

RFBR

Sirius University of Science and Technology, JSC Russian Railways and Educational Fund “Talent and success”

Russian government’s (9th competition) “Hybrid methods of modelling and optimization in complex systems”

Publisher

MDPI AG

Subject

Information Systems

Reference37 articles.

1. Supervised machine leaning: Are view of classification techniques;Kotsiantis;Informatica,2007

2. Toward Attribute Efficient Learning of Decision Lists and Parities;Klivans;J. Mach. Learn. Res.,2006

3. Classification and regression trees;Loh;Wiley Interdiscip. Rev. Data Min. Knowl. Discov.,2011

4. Vorontsov, K.V. (2022, June 07). Lectures on Logical Algorithms of Classification. Available online: http://www.machinelearning.ru/wiki/images/3/3e/Voron-ML-Logic.pdf.

5. Algorithm for learning pattern recognition “core”;Vaintsvaig;Algorithms Learn. Pattern Recognit.,1973

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