Name
Abstract | ||
|---|---|---|
Despite being a well-researched problem, ovulation detection in human female remains a difficult task. Most current methods for ovulation detection rely on measurements of a single property (e.g. morning body temperature) or at most on two properties (e.g. both salivary and vaginal electrical resistance). In this paper we present a machine learning based method for detecting the day in which ovulation occurs. Our method considered measurements of five different properties. We crawled a data-set from the web and showed that our method outperforms current state-of-the-art methods for ovulation detection. Our method performs well also when considering measurements of fewer properties. We show that our method's performance can be further improved by using unlabeled data, that is, mensuration cycles without a know ovulation date. Our resulted machine learning model can be very useful for women trying to conceive that have trouble in recognizing their ovulation period, especially when some measurements are missing. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICTAI.2019.00039 | 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) |
Keywords | Field | DocType |
semi supervised learning,ovulation detection | Ovulation,Semi-supervised learning,Pattern recognition,Computer science,Artificial intelligence,Machine learning,Ovulation Detection | Conference |
ISSN | ISBN | Citations |
1082-3409 | 978-1-7281-3799-5 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amos Azaria | 1 | 272 | 32.02 |
| Seagal Azaria | 2 | 0 | 0.34 |