ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1649740
This article is part of the Research TopicSmart Forecasting: Deep Learning and Explainable AI for Real-World Time Series PredictionView all articles
HairSentinel: A Time-Aware Anomaly Detection Framework for Hairfall Trend Forecasting Using Temporal Fusion Transformers 1
Provisionally accepted
- 1VIT University, Vellore, India
- 2Vellore Institute of Technology, Vellore, India
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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 that influence hairfall. Our research paper presents a novel approach to detect 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 paper 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 identify the most suitable one. TFT model is selected as the most suitable one with 97.5% accuracy and 97.4% precision over other models supporting anomaly detection. This allows us to establish normal margins of deviation from typical hair shedding cycles. This study enables proactive detection of anomalies, indicating sudden increase or decrease in hairfall due to hormonal fluctuations and the results support early identification of potential health risks before they get intensified and help suggest appropriate dietary plans.
Keywords: Hairfall Detection, time series analysis, anomaly detection, Hormonal imbalance, Predictive Modeling, health monitoring, Nutritional deficiency, Scalp health
Received: 18 Jun 2025; Accepted: 15 Sep 2025.
Copyright: © 2025 Leema, T, Sri G and P. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Balakrishnan P, balakrishnan.p@vit.ac.in
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