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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 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 evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.

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 study found that both desmoglein-specific and non-desmoglein autoantibodies may play active roles in Pemphigus vulgaris pathogenesis, with HLA genetics influencing autoimmune specificity.

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

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 identified over 250 new genetic loci linked to severe male pattern baldness, and developed a prediction algorithm that could accurately differentiate between those with severe and no hair loss.

This study developed an algorithm for predicting the impact of innovations on economic growth and reported its potential to enhance innovation policy decisions.

In this study, researchers used genetic data from over 52,000 men to identify over 250 genetic loci associated with severe hair loss and developed a predictive algorithm for determining hair loss risk.

In this study, researchers explored drug repurposing as a potential strategy for treating psoriasis and identified Pioglitazone, Trimipramine, and Dimetindene as promising candidates for this indication, based on molecular docking and predictive algorithms.

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.

In this study, a machine learning framework incorporating the CatBoost algorithm accurately predicted Systemic Lupus Erythematosus in Omani patients, suggesting potential for early clinical intervention.

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 reports that an Explainable AI-based hair health prediction system using a novel hybrid model outperformed traditional machine learning methods, achieving high accuracy in predicting key factors and providing personalized recommendations.

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

In this study, machine learning models showed high accuracy in predicting therapeutic outcomes for female pattern hair loss, highlighting the significant impact of age of onset and condition duration on treatment response.

In this study, researchers aimed to develop a Naive Bayes algorithm to predict hair loss risk based on personal and clinical data, including age, gender, stress levels, hormones, and family history. Results were not reported.

This study found that machine learning techniques, such as Random Forest, SVMs, and KNN, can significantly improve the early detection and determination of hair loss, potentially transforming treatment with more accurate and personalized approaches compared to traditional methods.

In this study, machine learning-based computer-aided diagnosis significantly improved accuracy in diagnosing alopecia areata compared to traditional visual methods, achieving up to 91.9% accuracy using different classifiers like CNN, SVM, and random forest models.

This study used machine learning to develop classifiers for identifying effective inhibitors of 5α-reductase isozyme 2, achieving high performance in distinguishing potent from weak inhibitors.

In this study, researchers introduced a machine learning approach to discover new nanozymes through the DiZyme platform, enabling the accurate prediction of multiple catalytic activities, and providing a comprehensive database and assistant resources for users.

This research reported that using J48 algorithms with bagging improves prediction accuracy of hair health through machine learning by analyzing factors like spatial-temporal images, gender, and age, achieving a real-time performance of 89.5%.

This study found that topical Vorinostat induced significant hair regrowth in mice with alopecia areata, suggesting it as a potential repurposable treatment for the condition.

This study presents a machine learning algorithm that achieved 89.5% accuracy in predicting hair health using factors like spatial-temporal images, age, and gender.

This study found that using at least one ICD-10 code for alopecia in Medicaid claims data accurately identified alopecia in women of childbearing age, with positive predictive values between 95.3% and 100% across various algorithms tested.

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 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 review discusses the increasing role of machine learning in drug repurposing, demonstrating how algorithms can reveal new therapeutic uses for existing drugs and predict side effects, with potential benefits for precision medicine, while also addressing ethical and privacy concerns.