Identification of Stress-Related Hair Loss and Prevention Through the Application of a KNN Model Based on Machine Learning

    August 2025 in “ International Journal of Research Publication and Reviews ”
    GuttiLokesh Kalyan, D S Mahesh
    TLDR Machine learning can predict stress-related hair loss and suggest prevention tips.
    The study presents a machine learning-based approach using the K-Nearest Neighbor (KNN) algorithm to predict hairfall severity and suggest preventive measures, focusing on stress-related factors such as workload, sleep quality, diet, scalp condition, and haircare habits. The model classifies hairfall into four categories: Few, Medium, Many, and A Lot, and provides personalized prevention recommendations. The results show a strong correlation between stress intensity and hairfall severity, highlighting the potential of machine learning in preventive medicine. The study offers an inexpensive and accessible solution, with future plans to include scalp image analysis and integration with wearable stress monitors.
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