An Early Hair Loss Detection And Prediction Method Based On Machine Learning

October 2025

Muhammad Umair Ahmad, Azka Mir, Anton Permana

hair loss machine learning Random Forest genetics trichology

Studysummary 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.

Automatically generated from the study's abstract, not written by a person, and not a review of the full paper. Not medical advice or a treatment recommendation. Read the original study, and consult a qualified healthcare professional before changing treatment. Full disclaimer

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This study demonstrated that a CNN-based model using VGG-16 architecture achieved a 94.5% accuracy in classifying ten types of hair diseases, implying a promising tool for aiding health professionals in diagnosing hair conditions accurately.

research Enhancing Hairfall Prediction: A Comparative Analysis of Individual Algorithms and an Ensemble Method

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research AB-MTE Deep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata

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research Scalp Disease Analysis Using Deep Learning Models

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research Hair and Scalp Disease Detection Using Machine Learning and Image Processing

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research Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN

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This study reported that a new deep learning algorithm using Mask R-CNN improved hair follicle classification accuracy by 4 to 15%, suggesting potential clinical application for enhanced hair loss diagnosis.

research Deep Review on Alopecia Areata Diagnosis for Hair Loss-Related Autoimmune Disorder

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This research explored the use of machine learning and deep learning methods to accurately identify alopecia areata in humans by analyzing facial images and demonstrated the potential of these techniques for medical, security, and commercial applications.

research Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

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research Classifications of Patterned Hair Loss: A Review

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This review discusses various classification systems for patterned hair loss in both sexes and reports no new clinical findings, highlighting the Hamilton-Norwood and Ludwig systems as the most commonly used.