Semi-supervised ovulation detection based on multiple properties

Amos Azaria, Seagal Azaria

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء

1 اقتباس (Scopus)

ملخص

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.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيفProceedings - IEEE 31st International Conference on Tools with Artificial Intelligence, ICTAI 2019
ناشرIEEE Computer Society
الصفحات222-228
عدد الصفحات7
رقم المعيار الدولي للكتب (الإلكتروني)9781728137988
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - نوفمبر 2019
الحدث31st IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2019 - Portland, الولايات المتّحدة
المدة: ٤ نوفمبر ٢٠١٩٦ نوفمبر ٢٠١٩

سلسلة المنشورات

الاسمProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
مستوى الصوت2019-November
رقم المعيار الدولي للدوريات (المطبوع)1082-3409

!!Conference

!!Conference31st IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2019
الدولة/الإقليمالولايات المتّحدة
المدينةPortland
المدة٤/١١/١٩٦/١١/١٩

بصمة

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