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Review

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

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Department of Physics and Mathematics, University of Alcalá, 28805 Alcalá de Henares, Spain
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Department of Computer Science, University of Alcalá, 28805 Alcalá de Henares, Spain
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Ramón y Cajal Institute of Sanitary Research, 28034 Madrid, Spain
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Department of Technology of Computers and Communications, University of Extremadura, 10003 Cáceres, Spain
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Department of Nursing and Physiotherapy, University of Alcalá, 28805 Alcalá de Henares, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(15), 5135; https://doi.org/10.3390/app10155135
Received: 31 May 2020 / Revised: 17 July 2020 / Accepted: 24 July 2020 / Published: 26 July 2020
(This article belongs to the Special Issue The Application of Data Mining to Health Data)
Human 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. View Full-Text
Keywords: human disease; machine learning; data mining; artificial intelligence; big data human disease; machine learning; data mining; artificial intelligence; big data
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MDPI and ACS Style

Caballé-Cervigón, N.; Castillo-Sequera, J.L.; Gómez-Pulido, J.A.; Gómez-Pulido, J.M.; Polo-Luque, M.L. Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review. Appl. Sci. 2020, 10, 5135. https://doi.org/10.3390/app10155135

AMA Style

Caballé-Cervigón N, Castillo-Sequera JL, Gómez-Pulido JA, Gómez-Pulido JM, Polo-Luque ML. Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review. Applied Sciences. 2020; 10(15):5135. https://doi.org/10.3390/app10155135

Chicago/Turabian Style

Caballé-Cervigón, Nuria, José L. Castillo-Sequera, Juan A. Gómez-Pulido, José M. Gómez-Pulido, and María L. Polo-Luque. 2020. "Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review" Applied Sciences 10, no. 15: 5135. https://doi.org/10.3390/app10155135

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