With further research, early identification may lead to earlier intervention
By Lori Solomon HealthDay Reporter
WEDNESDAY, Jan. 22, 2025 (HealthDay News) — Machine learning models may be able to identify women with severe subjective cognitive decline (SCD) during the menopause transition, according to a study published online Jan. 14 in Menopause.
Xiangyu Zhao, from Shandong University in Jinan, China, and colleagues developed and validated a machine learning model for identifying individuals experiencing severe SCD during the menopause transition. The analysis included data from 1,264 nurses.
The researchers found that the Bortua algorithm (a feature-selection algorithm) identified 13 significant associated factors. The support vector machine exhibited the best overall performance out of the seven models, achieving an area under the receiver operating characteristic curve of 0.846, accuracy of 0.789, sensitivity of 0.753, specificity of 0.802, and an F1 score of 0.658. Menopausal symptoms and the stage of menopause were the two variables most strongly associated with SCD.
“This study highlights how the use of machine learning can be employed to identify women experiencing severe SCD during the menopause transition and potential associated factors,” Stephanie Faubion, M.D., medical director of The Menopause Society, said in a statement. “Early identification of high-risk persons may allow for targeted interventions to protect cognitive health. Future studies involving objective measures of cognition and longitudinal follow-up are crucial to better understanding these associations.”
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