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.
The models can help find better inhibitors for conditions like baldness and prostate disorders.