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In this study, the XGBoost algorithm successfully diagnosed polycystic ovary syndrome with an accuracy of 0.87 using a dataset from Kerala, suggesting its usefulness for classification problems in healthcare.

This study found that the DNN-DTIs prediction model achieved high accuracy in predicting drug-target interactions, suggesting its potential application in drug repositioning and the discovery of new uses for existing drugs.

This study observed that the binding of certain hormones in rat skin varies cyclically with the hair cycle phases, with testosterone showing maximum binding in the catagen phase.

In this study using a porcine skin model, the researchers reported that smaller curcumin nanocrystals in xanthan gum gel showed higher passive skin penetration, while larger particles achieved deeper follicular accumulation.

In this study, researchers developed a predictive model for the onset of alopecia areata by analyzing six datasets to identify key feature genes and employing various machine learning algorithms, ultimately finding the XGBoost model most effective for clinical application.