The document discusses the development of a predictive model for hair loss using the Random Forest Algorithm, which is a machine learning technique. This model aims to provide accurate and durable predictions by analyzing complex datasets that include factors such as genetics, hormones, lifestyle, and environment, all of which contribute to hair loss. The goal is to address the concerns of millions affected by hair loss, which can lead to anxiety and reduced self-esteem.
5 citations
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July 2019 in “Applied statistics/Journal of the Royal Statistical Society. Series C, Applied statistics” Case-only trees and random forests improve predictions of treatment effects in clinical trials.
20 citations
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September 2020 in “International journal of computer applications” The Random Forest algorithm was the most accurate at diagnosing Polycystic Ovarian Syndrome.
September 2023 in “JP Journal of Biostatistics” The random forest model effectively helps diagnose COVID-19 using key factors like age and symptoms.
2 citations
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September 2023 in “JMIR. Journal of medical internet research/Journal of medical internet research” Machine learning can predict symptoms and quality of life in chronic skin disease patients using smartphone app data, and shows that app use varies with patient characteristics.