Repository logo
Communities & Collections
All of DSpace
  • English
  • العربية
  • বাংলা
  • Català
  • Čeština
  • Deutsch
  • Ελληνικά
  • Español
  • Suomi
  • Français
  • Gàidhlig
  • हिंदी
  • Magyar
  • Italiano
  • Қазақ
  • Latviešu
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Српски
  • Svenska
  • Türkçe
  • Yкраї́нська
  • Tiếng Việt
Log In
New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Ali, Hussein"

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Results Per Page
  • Sort Options
  • Thumbnail Image
    Item
    Development and Performance Analysis of Machine Learning Methods for Predicting Depression Among Menopausal Women
    (Elsevier, 2023-05-25) Ali, Md. Mamun; Ali, Hussein; Algashamy, A.; Alzidi, Enas; Ahmed, Kawsar; Bui, Francis M.; Patel, Shobhit K.; Azam, Sami; Abdulrazak, Lway Faisal; Moni, Mohammad Ali
    Menopause is an obligatory phenomenon in a woman’s life. Some women face mental and physical issues during their menopausal period. Depression is one of the issues some women struggle with during their menopausal period. The scarcity of specialists, lack of knowledge, and awareness is the motivating factor in this research to predict depression among menopausal women and enhance their quality of life. The prediction of depression symptoms among menopausal women with machine learning techniques is promising and challenging in artificial intelligence. This study develops a system with significant accuracy using a supervised machine-learning approach. Various classification algorithms are used to determine the best-performing classifier by evaluating multiple parameters, including accuracy, sensitivity, specificity, precision, recall, F-Measure, Receiver Operating Characteristic (ROC), Precision–Recall​ Curve (PRC), and Area Under the Curve (AUC). We found that Random Forest and XGBoost classifiers are the performers with 99.04% accuracy employing the 14 most significant features.

© Open Research Bangladesh

  • Privacy policy
  • End User Agreement
  • Send Feedback