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

    October 2025
    Muhammad Umair Ahmad, Azka Mir, Anton Permana
    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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    Research cited in this study 10

    1. CNN-KNN Model for Assessing Hair Health in Telogen Effluvium 2025
    2. Hair Disease Classification Using Convolutional Neural Network Algorithm with VGG-16 Architecture Sinkron · 2023
    3. Enhancing Hairfall Prediction: A Comparative Analysis of Individual Algorithms and an Ensemble Method International journal on recent and innovation trends in computing and communication · 2023
    4. AB-MTE Deep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata Engineering Technology & Applied Science Research · 2023
    5. Scalp Disease Analysis Using Deep Learning Models Applied and Computational Engineering · 2023
    6. Hair and Scalp Disease Detection Using Machine Learning and Image Processing European Journal of Information Technologies and Computer Science · 2023
    7. Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN Journal of Imaging · 2022
    8. Deep Review on Alopecia Areata Diagnosis for Hair Loss-Related Autoimmune Disorder International Journal of Applied Pharmaceutics · 2022
    9. Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks Sensors · 2022
    10. Classifications of Patterned Hair Loss: A Review Journal of Cutaneous and Aesthetic Surgery · 2016