Predicting Hair Loss with AI: A Deep Learning Framework Combining Genetic and Scalp Health Data
DOI:
https://doi.org/10.48001/978-81-966500-0-1-6Keywords:
Hair Loss Prediction, Long Short-Term Memory, Deep Learning, Output LayerAbstract
Hair loss, affecting millions globally, stems from complex interactions between genetic, hormonal, environmental, and lifestyle factors.In this study, we propose a deep learning-based approach to predict hair loss by integrating various data sources, including genetic markers, hormonal profiles, scalp health, and lifestyle information. Convolutional Neural Networks (CNNs) are employed for feature extraction from high-resolution scalp images, enabling the identification of thinning patterns and follicle health. Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, are utilized to model temporal sequences of lifestyle and health data, capturing longitudinal patterns in hair loss progression.
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