HairSentinel: a time-aware anomaly detection framework for forecasting hairfall trends using temporal fusion transformers
A. Anny Leema, T. Saktheshwaran, G. Reena Sri, P. Balakrishnan
TL;DR
HairSentinel uses advanced models to detect unusual hairfall trends over time, helping identify health risks early.
Contribution
HairSentinel introduces a time-aware anomaly detection framework using Temporal Fusion Transformers for forecasting hairfall trends.
Findings
The Temporal Fusion Transformer (TFT) model achieved 97.5% accuracy and 97.4% precision in detecting hairfall anomalies.
The framework enables proactive detection of sudden changes in hairfall linked to hormonal fluctuations.
The model helps identify health risks early and suggests dietary plans based on detected anomalies.
Abstract
Hairfall is a primary concern for many individuals worldwide today. Hair strands may fall due to various conditions such as hereditary factors, scalp health issues, nutritional deficiencies, hormonal fluctuations, or irregular sleep cycles. Our study presents a novel approach to detecting hairfall trends over time. While traditional methods infer hairfall rates using CNN and SVM models—classifying types of hairfall based on high-resolution images and complex techniques—this study addresses the issue by analyzing user-provided data through simple, straightforward questions, maintaining ease of use. Each attribute is collected using a time-centric approach on a daily or weekly basis. For time series anomaly detection, we utilize LSTM, Random Forest, and the Temporal Fusion Transformer (TFT) to model hairfall fluctuations and compare them with the ARIMAX model across various metrics to…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Time Series Analysis and Forecasting · Complex Systems and Time Series Analysis