September 2025 in “Matics Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology)” This study found that among various predictive models for baldness risk, Random Forest Regression performed best with the lowest mean squared error and highest R², indicating strong predictive accuracy, especially with complex datasets, while Linear Regression was better suited to simpler datasets.
This study found a significant correlation between age and androgen alopecia levels in male patients with COVID-19, and it suggests an association between gender and androgen alopecia prevalence in this hospitalized group.
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March 2019 in “International Journal of Cosmetic Science” In this study, researchers developed a regression model that reasonably predicts consumer-perceived hair breakage using various parameters such as hair smoothness, detangling forces, extensional strength, and hair density among Indian women.
April 2019 in “Molecular Informatics” This study employed multiple linear regressions to analyze hydantoin analogues and produced a model with strong predictive abilities for designing new androgen receptor modulators.
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June 2017 in “Journal of The American Academy of Dermatology” PRP treatment for hair loss shows promise, with 58% of patients satisfied and most noticing improvement within 6 months.