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dc.contributor.authorCaballe Cervigón, Nuria 
dc.contributor.authorCastillo Sequera, José Luis 
dc.contributor.authorGómez Pulido, Juan Antonio 
dc.contributor.authorGómez Pulido, José Manuel 
dc.contributor.authorPolo Luque, María Luz
dc.identifier.bibliographicCitationCaballé, N.C., Castillo-Sequera, J.L., Gómez-Pulido, J.A., Gómez-Pulido, J.M. & Polo-Luque, M.L. 2020, “Machine learning applied to diagnosis of human diseases: a systematic review”, Applied Sciences, vol. 10, no. 15, 5135
dc.description.abstractHuman healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.en
dc.description.sponsorshipEuropean Commissionen
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)*
dc.subjectHuman diseaseen
dc.subjectMachine learningen
dc.subjectData miningen
dc.subjectArtificial intelligenceen
dc.subjectBig dataen
dc.titleMachine learning applied to diagnosis of human diseases: a systematic reviewen
dc.subject.ecienciaComputer scienceen
dc.contributor.affiliationUniversidad de Alcalá. Departamento de Ciencias de la Computaciónes_ES
dc.contributor.affiliationUniversidad de Alcalá. Departamento de Enfermería y Fisioterapiaes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7-INCO/ELAC2015%T09-0819/EU/Design and implementation of a low-cost smart system for pre-diagnosis and telecare of infectious diseases in elderly people/SPIDEPen
dc.identifier.publicationtitleApplied Sciences-Basel

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