Hairsentinel: A Time-Aware Anomaly Detection Framework for Forecasting Hairfall Trends Using Temporal Fusion Transformers

The study introduces "HairSentinel," a framework utilizing Temporal Fusion Transformers (TFT) to predict hairfall trends with high accuracy (97.52%) by analyzing time-series data related to lifestyle, nutrition, and hormonal factors. Involving 750 participants, the framework integrates machine learning models like LSTM and Random Forest to detect anomalies and provide personalized dietary recommendations, aiming for early detection of health risks associated with hairfall. The model's robustness is demonstrated through its ability to identify hormonal imbalances and nutrient deficiencies, offering a scalable and practical approach for real-time hairfall monitoring and proactive intervention, with potential applications in public health research and the haircare industry.