Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/15/5135 |
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| author | Nuria Caballé-Cervigón José L. Castillo-Sequera Juan A. Gómez-Pulido José M. Gómez-Pulido María L. Polo-Luque |
| author_facet | Nuria Caballé-Cervigón José L. Castillo-Sequera Juan A. Gómez-Pulido José M. Gómez-Pulido María L. Polo-Luque |
| author_sort | Nuria Caballé-Cervigón |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T18:11:45Z |
| format | Article |
| id | doaj.art-1ba885c2a05149ada83fcbe9344bdaa4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T18:11:45Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1ba885c2a05149ada83fcbe9344bdaa42023-11-20T08:01:05ZengMDPI AGApplied Sciences2076-34172020-07-011015513510.3390/app10155135Machine Learning Applied to Diagnosis of Human Diseases: A Systematic ReviewNuria Caballé-Cervigón0José L. Castillo-Sequera1Juan A. Gómez-Pulido2José M. Gómez-Pulido3María L. Polo-Luque4Department of Physics and Mathematics, University of Alcalá, 28805 Alcalá de Henares, SpainDepartment of Computer Science, University of Alcalá, 28805 Alcalá de Henares, SpainDepartment of Technology of Computers and Communications, University of Extremadura, 10003 Cáceres, SpainDepartment of Computer Science, University of Alcalá, 28805 Alcalá de Henares, SpainRamón y Cajal Institute of Sanitary Research, 28034 Madrid, SpainHuman 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.https://www.mdpi.com/2076-3417/10/15/5135human diseasemachine learningdata miningartificial intelligencebig data |
| spellingShingle | Nuria Caballé-Cervigón José L. Castillo-Sequera Juan A. Gómez-Pulido José M. Gómez-Pulido María L. Polo-Luque Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review Applied Sciences human disease machine learning data mining artificial intelligence big data |
| title | Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review |
| title_full | Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review |
| title_fullStr | Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review |
| title_full_unstemmed | Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review |
| title_short | Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review |
| title_sort | machine learning applied to diagnosis of human diseases a systematic review |
| topic | human disease machine learning data mining artificial intelligence big data |
| url | https://www.mdpi.com/2076-3417/10/15/5135 |
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