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

• Published in: Information (Basel) 2022-12, Vol.13 (12), p.589
• Main Authors: Kuzmich, Roman, Stupina, Alena, Yasinskiy, Andrey, Pokushko, Mariia, Tsarev, Roman, Boubriak, Ivan
• Format: Article
• Language: English
• Subjects: Algorithms; boosting criterion; Classification; classifier; Classifiers; Decision making; Decision trees; Digitization; Information systems; Machine learning; optimization model; Optimization models; pattern; prediction and diagnostic problems; Sustainable development
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info13120589

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.