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This study found that people with systemic lupus erythematosus consulted primary care more frequently and with specific clinical features before diagnosis, and it developed a risk prediction model that may help in identifying at-risk individuals.

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.

In this study, researchers evaluated various machine learning models for predicting type 2 diabetes risk, finding that Random Forest and K-NN models performed best in terms of precision, recall, accuracy, and other metrics using common symptoms as features.

This study found that a family history of androgenetic alopecia and specific trichoscopic signs are strong predictors of female pattern hair loss, leading to a nomogram model for risk prediction.

This study found that a family history of androgenetic alopecia and specific trichoscopic features are key factors in predicting female pattern hair loss, and the developed nomogram model may help improve disease management by assessing these risks.