Development and performance analysis of machine learning methods for predicting depression among menopausal women
Open Access
- 31 May 2023
- journal article
- research article
- Published by Elsevier in Healthcare Analytics
- Vol. 3
Abstract
No abstract availableKeywords
Funding Information
- Natural Sciences and Engineering Research Council of Canada
This publication has 54 references indexed in Scilit:
- Metabolic syndrome among pre- and post-menopausal rural women in Bangladesh: result from a population-based studyBMC Research Notes, 2013
- Mood and Menopause: Findings from the Study of Women's Health Across the Nation (SWAN) over 10 YearsObstetrics and Gynecology Clinics of North America, 2011
- Genome Wide Association Study to predict severe asthma exacerbations in children using random forests classifiersBMC Medical Genetics, 2011
- Characterising and Predicting Haploinsufficiency in the Human GenomePLOS Genetics, 2010
- The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive modelsNature Biotechnology, 2010
- A multilevel data integration resource for breast cancer studyBMC Systems Biology, 2010
- On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional dataBioinformatics, 2010
- Assessment of menopausal symptoms using modified Menopause Rating Scale (MRS) among middle age women in Kuching, Sarawak, MalaysiaAsia Pacific Family Medicine, 2010
- Depressive symptoms during the menopausal transition: The Study of Women's Health Across the Nation (SWAN)Journal of Affective Disorders, 2007
- Does menopausal transition affect the quality of life? A longitudinal study of middle-aged women in KinmenMenopause, 2007