RT info:eu-repo/semantics/article T1 Machine learning applied to diagnosis of human diseases: a systematic review A1 Caballe Cervigón, Nuria A1 Castillo Sequera, José Luis A1 Gómez Pulido, Juan Antonio A1 Gómez Pulido, José Manuel A1 Polo Luque, María Luz K1 Human disease K1 Machine learning K1 Data mining K1 Artificial intelligence K1 Big data K1 Informática K1 Computer science K1 Medicina K1 Medicine AB 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. PB MDPI SN 2076-3417 YR 2020 FD 2020-07-26 LK http://hdl.handle.net/10017/43940 UL http://hdl.handle.net/10017/43940 LA eng NO European Commission DS MINDS@UW RD 29-mar-2024