This study utilized the Random Forest Algorithm to create a machine learning model aimed at accurately predicting hair loss by considering complex datasets involving genetic, hormonal, lifestyle, and environmental factors, but specific outcomes were not reported.
June 2023 in “International journal on recent and innovation trends in computing and communication” This study found that ensemble machine learning models effectively predict hair fall by combining the strengths of individual algorithms, leading to higher accuracy, precision, and recall in identifying hair and non-hair fall instances compared to single algorithms.
5 citations
,
July 2023 in “Journal of Autonomous Intelligence” This study evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.
This study evaluated machine-learning models to predict PCOS among reproductive-aged women in Bangladesh, finding that the XGBoost model achieved high accuracy (99.63%) and effectiveness, particularly when prioritizing clinical features over psychological ones in the predictive process.
July 2025 in “Journal of Investigative Dermatology” This study found that both desmoglein-specific and non-desmoglein autoantibodies may play active roles in Pemphigus vulgaris pathogenesis, with HLA genetics influencing autoimmune specificity.