چکیده مقاله
Introduction: Clustering analysis can help in identification of at risk groups The study aimed to identify clusters of midlife women by their similarity of menopausal severity symptoms Method: In this cross sectional study, 664 women living in Mashhad, Iran were collected The Menopause Severity Symptoms Inventory was used to collect information about menopausal symptoms A clustering algorithm was applied to classify women with different menopausal symptoms Result: k means clustering algorithm, extracted three major clusters based on different menopausal symptoms The first cluster involved 301 45% women with mild symptoms, the second was a cluster of moderate symptoms women with size 131 20% The remaining 232 35% of women were placed in the third cluster Conclusion: Three major clusters of women were identified The study revealed a high prevalence of pain in muscles and joints, anxiety, and vasomotor symptoms among Iranian women, so promoting women's self care and some interventions could alleviate these issues
کلیدواژهها
نویسندگان
Fahimeh Hoseinzadeh
Ph D Candidate of Biostatistics, Mashhad, Iran
Habibollah Esmaily
School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
Sedigheh Ayatiafin
Ghaem Hospital,Mashhad University of Medical Sciences, Mashhad, Iran
Azadeh Saki
School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
شیوه ارجاع
Hoseinzadeh, Fahimeh and Esmaily, Habibollah and Ayatiafin, Sedigheh and Saki, Azadeh,1403,Clustering Iranian women according to their Menopausal Severity Symptoms (MSSI-38),1st International Biennial Conference of Artificial Intelligence and Data Science 2024,Bushehr
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی دوسالانه هوش مصنوعی و علوم داده3 اردیبهشت 1403 · بوشهر