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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 devised a stochastic hair growth model influenced by regular haircuts, using a process that undergoes linear growth subject to random resets, and used it to theoretically determine an ideal haircut routine.

This study explored using large language models to automatically identify monkeypox cases from electronic medical records, finding that models based on DeepSeek features performed better than traditional methods, with logistic regression showing high accuracy in detecting key symptoms like fever and rash.

This study found that integrating the Theory of Planned Behaviour with the Stimulus-Organism-Response framework improved the prediction of intentions to buy embarrassing products, with subjective norms being significant across drugstore, internet, and multi-channel environments.

The researchers reported that differences in brain functional connectivity between unmedicated seasonal affective disorder patients and healthy controls vary with ICA model order, peaking in volume at model order 70.

This study applied a novel Cox regression subsampling method to massive datasets, demonstrated using UK-biobank colorectal cancer data, effectively reducing computation time and memory usage while building a risk-prediction model under certain conditions involving right-censored and potentially left-truncated data with rare events.

This study presents evidence suggesting that androgenetic alopecia is most likely inherited through a polygenic model, challenging the traditional view that it is caused by a simple Mendelian autosomal dominant gene.

This paper discusses the lack of a genetic model for androchronogenic alopecia in rodents, noting the stumptailed macaque as a better current model due to its similarities to human male-pattern baldness; it reports no new results.

This study reviews methods to ensure the reliability of machine learning models in medical imaging, focusing on bias detection, data drift assessment, and accuracy estimation without ground truth labels to enhance integration into clinical settings.

This review systematically evaluates 13 animal and 2 mathematical models for alopecia areata, assessing their effectiveness in understanding the condition's pathogenesis and aiding in the development of new therapeutic strategies.

In this study, researchers developed a mathematical model to explore how immune system dynamics affect hair follicle behavior in alopecia areata, identifying key parameters influencing disease progression and suggesting potential treatment strategies targeting immune dysregulation.

This study developed a model for detecting sparse hair clusters using enhanced object detection neural networks and medical images, which accurately identifies and counts sparse hair clusters with greater accuracy and efficiency than existing methods.

In this study, researchers found that the original four-factor model of causal illness attributions in the IPQ-R did not fit a Chinese sample, and instead identified a two-factor model focusing on psychological attributions and risk factors.

This study found that the WNMFDDA model effectively predicts drug-disease associations, achieving high accuracy in cross-validation and confirming most candidate associations through existing databases.

This study developed and analyzed a mathematical model for alopecia areata involving interactions among immune cells and cytokines, establishing conditions under which hairless patches can become stable and pervasive, particularly with certain biological rate levels and chemotactic sensitivities.

This study describes the development of a prototype clinical expert system that uses a belief rule-based inference methodology to improve the risk stratification and diagnosis of polycystic ovary syndrome by addressing uncertainties in clinical data and domain knowledge.

This study introduces a novel computational model using fuzzy logic and multi-criteria decision-making techniques to create a triage system for androgenetic alopecia management, stratifying patients into seven severity levels and aiding in resource allocation and treatment planning.

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.

This study suggests that immune privilege guardians and the pro-inflammatory cytokine interferon-γ significantly influence the dynamics of alopecia areata, supporting the hypothesis that immune privilege collapse is crucial in its development.

This study developed a mathematical model to simulate immune interactions and hair cycle dynamics in alopecia areata, identifying influential factors affecting hair growth and offering insights into potential treatment strategies by targeting immune dysregulation.

This study found that an ML model incorporating factors like Breslow thickness and age improved cutaneous malignant melanoma prognosis predictions compared to TNM staging, with a C-index of 80% versus 66.6% for TNM alone, suggesting ML's potential for personalized prognostication.

This study evaluated machine-learning models to predict PCOS among reproductive-aged women in Bangladesh, finding that the XGBoost model achieved high accuracy (99.63%) and effectiveness, particularly when prioritizing clinical features over psychological ones in the predictive process.

This article discusses a proposed "Darwinian" model of carcinogenesis and emphasizes that cancer prevention involves more than avoiding mutagens, as gene-environment interactions are complex and non-linear.

In this study, researchers developed a mathematical model of outer hair cell hair bundles that replicates experimental responses to mechanical stimuli, providing insights into the stimulus-dependent adaptation mechanisms and the functional significance of their three-row stereocilia configuration in mammalian hearing.

This study utilized the Random Forest Algorithm to create a machine learning model aimed at accurately predicting hair loss by considering complex datasets involving genetic, hormonal, lifestyle, and environmental factors, but specific outcomes were not reported.

This article argues that the mind-body problem is a false issue, proposing that the mind merely temporally associates immaterial data with material support without actual interaction.

This article examines the emergence of predictive analytics with big data and concludes that Foucault's concept of biopower is now a hybrid involving various technologies to monitor and model behavior and risk.

This mathematical model of alopecia areata illustrates how inflammatory autoimmune responses interrupt the anagen phase of hair growth, and may help evaluate treatments and identify new therapeutic targets.