research Predictive Modeling of Hair Fall using Random Forest Algorithms
The model accurately predicts hair loss by analyzing various factors.
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research Comparison of Linear Regression, Decision Tree Regression, and Random Forest Regression Algorithms in Predicting Baldness Risk
Random Forest Regression is best for predicting baldness risk.
research Comparative Analysis of Machine Learning Algorithms in Diagnosis of Polycystic Ovarian Syndrome
The Random Forest algorithm was the most accurate at diagnosing Polycystic Ovarian Syndrome.
research An Early Hair Loss Detection and Prediction Method Based on Machine Learning
Machine learning can accurately predict hair loss early, improving treatment options.
research BaldPredict: AI-Driven Prediction Model for Alopecia Areata
AI can improve alopecia areata diagnosis with high accuracy.
research In Search of Predictive Models for Inhibitors of 5-alpha Reductase 2 Based on the Integration of Bioactivity and Molecular Descriptors Data.
The models can help find better inhibitors for conditions like baldness and prostate disorders.
research A DEEP LEARNING APPROACH FOR DIAGNOSIS OF COVID-19 INFECTION AND ITS RELATED FACTORS: A POPULATION-BASED STUDY
The random forest model effectively helps diagnose COVID-19 using key factors like age and symptoms.
research ARTIFICIAL INTELLIGENCE TECHNIQUES IN PREDICTION OF COVID-19 MORTALITY AND ITS RELATED FACTORS: A MULTI-CENTER STUDY
AI can effectively predict COVID-19 mortality risk using patient data.
research Case-Only Trees and Random Forests for Exploring Genotype-Specific Treatment Effects in Randomized Clinical Trials with Dichotomous End Points
Case-only trees and random forests improve predictions of treatment effects in clinical trials.
research Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study
Machine learning can predict symptoms and quality of life in chronic skin disease patients using smartphone app data, and shows that app use varies with patient characteristics.
research LEVERAGING MACHINE LEARNING METHODS IN PREDICTING AND ANALYZING THE ASSOCIATION BETWEEN DIETARY INFLAMMATORY INDEX AND ALOPECIA
Inflammatory diets may increase the risk and severity of alopecia areata.
research Estimating Scalp Moisture in a Hat Using Wearable Sensors
A hat with sensors can measure scalp moisture well, helping with hair care.
research Diagnosis of childhood and adolescent growth hormone deficiency using transcriptomic data
Childhood growth hormone deficiency can be accurately diagnosed using gene expression data and random forest analysis.
research HairSentinel: a time-aware anomaly detection framework for forecasting hairfall trends using temporal fusion transformers
"HairSentinel" accurately detects hairfall trends using simple user data, helping identify health risks early.
research Machine Learning--Enhanced Pharmacovigilance Assessment of Cardiac Adverse Events Associated With Minoxidil
Minoxidil is strongly linked to heart problems, and machine learning can improve drug safety checks.
research Two-Stage Machine Learning-Based GWAS for Wool Traits in Central Anatolian Merino Sheep
Machine learning can effectively identify genes to improve wool quality in sheep.
research Enhancing Hairfall Prediction: A Comparative Analysis of Individual Algorithms and An Ensemble Method
Combining multiple algorithms predicts hair fall more accurately than using single algorithms.
research Penerapan Algoritma Naive Bayes untuk Prediksi Kerontokan Rambut
Naive Bayes algorithm can help predict hair loss risk early.
research Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture
The system can accurately classify hair diseases with 94.5% accuracy using a CNN.
research Construction of regulatory network for alopecia areata progression and identification of immune monitoring genes based on multiple machine-learning algorithms
Researchers found four genes that could help diagnose severe alopecia areata early.
research Assessment of college students’ mental health status based on temporal perception and hybrid clustering algorithm under the impact of public health events
A new method accurately measures college students' mental health by considering time perception and clustering techniques.
research Research on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering
The FAW-FS algorithm improves depression recognition, and psychological interventions help AGA patients' mental health.
research Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review
Computer vision techniques can help detect and assess skin conditions like vitiligo, alopecia areata, and dermatitis.
research A machine learning approach for non-invasive PCOS diagnosis from ultrasound and clinical features
Machine learning can accurately diagnose PCOS non-invasively using clinical and ultrasound features.
research Early prediction of alopecia areata using machine learning modeling of neuro stress immune signatures from multi datasets
A machine learning model can predict alopecia areata early using specific gene markers.
research Machine Learning Approach for Predicting Systemic Lupus Erythematosus in Oman-based Cohort
The machine learning model accurately predicts Systemic Lupus Erythematosus in Omani patients.
research DiZyme: The Ultimate Resource for Nanozyme Multiple Catalytic Activity Prediction
DiZyme accurately predicts nanozyme activities to aid in discovering new applications.
research Emerging smart microneedle technologies in psoriasis: convergence of nanocarriers, machine learning, and personalized delivery
Smart microneedles using advanced tech could improve psoriasis treatment.
research Poster Abstracts
The model and estimator can predict drug exposure in kidney transplant patients well.
research The Transformative Role of Artificial Intelligence in Plastic and Reconstructive Surgery: Challenges and Opportunities
AI can greatly improve plastic surgery, but ethical care and human aspects must remain a priority.