Back to Search
Start Over
Machine Learning (ML) based-method applied in recurrent pregnancy loss (RPL) patients diagnostic work-up: a potential innovation in common clinical practice
- Source :
- Scientific Reports, Vol 10, Iss 1, Pp 1-12 (2020), Scientific Reports
- Publication Year :
- 2020
- Publisher :
- Nature Publishing Group, 2020.
-
Abstract
- RPL is a very debated condition, in which many issues concerning definition, etiological factors to investigate or therapies to apply are still controversial. ML could help clinicians to reach an objectiveness in RPL classification and access to care. Our aim was to stratify RPL patients in different risk classes by applying an ML algorithm, through a diagnostic work-up to validate it for the appropriate prognosis and potential therapeutic approach. 734 patients were enrolled and divided into 4 risk classes, according to the numbers of miscarriages. ML method, called Support Vector Machine (SVM), was used to analyze data. Using the whole set of 43 features and the set of the most informative 18 features we obtained comparable results: respectively 81.86 ± 0.35% and 81.71 ± 0.37% Unbalanced Accuracy. Applying the same method, introducing the only features recommended by ESHRE, a correct classification was obtained only in 58.52 ± 0.58%. ML approach could provide a Support Decision System tool to stratify RPL patients and address them objectively to the proper clinical management.
- Subjects :
- 0301 basic medicine
Adult
Abortion, Habitual
Support Vector Machine
Adolescent
Reproductive disorders
Computer science
Clinical Decision-Making
MEDLINE
lcsh:Medicine
Machine learning
computer.software_genre
Settore MED/04
Article
Machine Learning
03 medical and health sciences
Therapeutic approach
Young Adult
0302 clinical medicine
Pregnancy
medicine
Humans
Set (psychology)
lcsh:Science
030219 obstetrics & reproductive medicine
Multidisciplinary
business.industry
lcsh:R
Abortion
Disease Management
Translational research
Middle Aged
medicine.disease
Work-up
Habitual
Support vector machine
Clinical Practice
030104 developmental biology
Female
lcsh:Q
Artificial intelligence
business
Algorithms
Biomarkers
computer
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 10
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....7b6b310b3ac05a45b7a85e14609e618f