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.
5 citations
,
July 2019 in “Applied statistics/Journal of the Royal Statistical Society. Series C, Applied statistics” In this study, applying case-only trees and random forests to a prostate cancer prevention trial revealed genotypes that may influence the efficacy of finasteride for prostate cancer prevention.
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.
2 citations
,
September 2023 in “JMIR. Journal of medical internet research/Journal of medical internet research” This study reported that AutoML effectively modeled itching and pain development, as well as app use, in patients with chronic eczema or psoriasis using a smartphone monitoring app, revealing that factors like BMI, age, and disease activity significantly influenced app engagement.
This study used machine learning to develop classifiers for identifying effective inhibitors of 5α-reductase isozyme 2, achieving high performance in distinguishing potent from weak inhibitors.