Machine Learning Algorithm Selection for a Clinical Decision Support System Based on a Multicriteria Method

Autor:	Galo Enrique Valverde Landivar, Jonathan Andrés España Arambulo, Maikel Leyva Vázquez, Miguel Angel Quiroz Martinez
Rok vydání:	2021
Předmět:	Data collection Computer science business.industry Exploratory research Analytic hierarchy process TOPSIS Machine learning computer.software_genre Clinical decision support system Algorithm Selection Support vector machine Artificial intelligence business computer Selection (genetic algorithm)
Zdroj:	Human Interaction, Emerging Technologies and Future Systems V ISBN: 9783030855390 IHIET (Paris)
DOI:	10.1007/978-3-030-85540-6_128
Popis:	On the current information in the medical area related to cancer analysis, the selection of an optimal Machine Learning algorithm, based on a multicriteria method, for a system that supports clinical decisions is sought. As a methodology, exploratory research and the deductive method were applied to analyze the information from existing articles and ML algorithms' behavior applied in the area of medicine. This research and based on a use case of training and testing of the GLM, SVM, and ANN algorithms for selecting an algorithm. Addition-ally, for clinical decisions, and architecture prototype for medical data collection is presented resulted. Based on AHP and TOPSIS methods Support Vector Machine (SVM) is the best alternative.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f5cb4c38ea759e6b0f9fb16d40c14614 https://doi.org/10.1007/978-3-030-85540-6_128 Zobrazit plný text záznamu