Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

This study found that ensemble machine learning models effectively predict hair fall by combining the strengths of individual algorithms, leading to higher accuracy, precision, and recall in identifying hair and non-hair fall instances compared to single algorithms.

This study developed new genetic prediction models for male pattern baldness with improved accuracy by utilizing a large set of markers and independent datasets, making them the most reliable available for this trait.

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.

This paper proposes an integrated model suggesting that psychological stress, sleep disturbances, nutrition, lifestyle, and various biological factors collectively influence hair health, including hair loss and premature greying.

This study synthesizes evidence on the complex interplay between stress, sleep, nutrition, lifestyle, and systemic health and their impact on hair health, encompassing premature greying, hair loss, and follicle cycling.

This study in young Ukrainian men found that specific body measurements, like shoulder width and tibia epiphysis width, are highly predictive of alopecia areata occurrence, though not for its progression, using discriminant models (p<0.001).

In this study, researchers developed a deep learning model that efficiently classifies five degrees of harm with high accuracy, achieving up to 98% precision, recall, and F1-score across various harm levels, indicating strong potential for practical application in automated harm evaluation.

This study developed a model predicting uranium excretion in human hair from drinking water exposure, suggesting it is a viable indicator for assessing internal uranium burden.

This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.

This study found that a nomogram prediction model based on clinical characteristics, bone metabolism, and ovarian function can effectively predict the treatment response to long-acting GnRHa in girls with idiopathic central precocious puberty.

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 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 documented that a CNN-KNN hybrid model achieved 98% accuracy in predicting hair breakage levels due to Telogen Effluvium, highlighting its potential for enhancing diagnosis and treatment in clinical dermatology through early detection of hair-related conditions.

This study found that using trait prevalence-informed priors may improve the prediction accuracy of appearance traits in Bayesian models, but their application is limited by sparse knowledge on trait prevalence.

In this retrospective study, researchers developed machine learning models to predict mortality risk among 7115 COVID-19 patients in Iran, finding that the random forests model performed best with 96% accuracy and identified factors like intubation and SpO2 as significant predictors.

This study proposed a model that accurately predicts skin condition using genotype information and machine learning, suggesting potential for creating customized cosmetics.

This study found that the 4Kscore model, when adjusted for kallikrein marker changes due to finasteride use, improved the prediction of high-grade prostate cancer compared to adjusted total PSA alone in men taking 5-α-reductase inhibitors.

This study reports that a clinical prognostic model based on immune-related genes improved survival prediction for patients with clear cell renal cell carcinoma, also identifying potential drugs targeting the gene DOCK8.

This study suggests that histological features of primary melanoma can partially predict lymph node metastasis using AI, achieving a best prediction AUROC of 0.65.

In this study, the integration of obesity-related features and machine learning techniques significantly enhanced cardiovascular disease detection, with the XGBoost classifier achieving a 74% accuracy rate and improved metrics compared to other models.

This study developed and validated a radiomics-based clinical nomogram using routine renal ultrasound and clinical data to predict early diabetic kidney injury in a community cohort, finding it significantly outperformed clinical-only models in predicting risk and showed potential as a cost-effective screening tool.

This review discussed recent research on using machine learning to predict mental disorders, reporting that Adaboost could predict depression with 92.5% accuracy and 93.6% specificity, while other models like XGBoost and RNN were applied for post-stroke depression and EEG-based depression detection, respectively.

This study found that metabolism-related genes significantly impact the prognosis and metastasis in breast cancer, and the development of prediction models may guide personalized therapeutic strategies.

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.

This study developed a geometric framework for estimating safe donor harvesting in hair transplants, showing that individual hair characteristics like caliber and length significantly affect donor capacity.

This study employed multiple linear regressions to analyze hydantoin analogues and produced a model with strong predictive abilities for designing new androgen receptor modulators.

This study designed a novel model for 5a-reductase enzyme inhibitors using pharmacophore and 3D QSAR techniques, potentially allowing for improved prediction and development of drug therapies targeting benign prostatic hyperplasia.

In this study, researchers found a significant prevalence of nonalcoholic fatty liver disease in Egyptian women with polycystic ovary syndrome, with insulin resistance being a key predictive factor.