Clustering Iranian women according to their Menopausal Severity Symptoms (MSSI-۳۸)

  • سال انتشار: 1403
  • محل انتشار: اولین کنفرانس بین المللی دوسالانه هوش مصنوعی و علوم داده
  • کد COI اختصاصی: DSAI01_067
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 173
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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

چکیده

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, ۶۶۴ 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 ۳۰۱ (۴۵%) women with mild symptoms, the second was a cluster of moderate symptoms women with size ۱۳۱ (۲۰%). The remaining ۲۳۲ (۳۵%) 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.

کلیدواژه ها

Menopausal Severity Symptoms, MSSI-۳۸, K-Means Clustering, Classification

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