Enhancing Hairfall Prediction: A Comparative Analysis of Individual Algorithms and An Ensemble Method

Abstract

Hair fall, a prevalent issue affecting many individuals globally, necessitates early detection for preventive measures and hair health maintenance. Machine learning algorithms have gained attention in predicting hair fall by analysing genetic predisposition, lifestyle habits, and environmental factors. However, the performance of individual algorithms can be improved through ensemble models that combine their strengths. This research paper proposes an ensemble machine learning approach tailored for hair fall prediction. Comparative evaluations with individual algorithms reveal the ensemble models consistently outperform in accuracy, precision, and recall. Leveraging diverse algorithms, the ensemble approach captures a wider range of hair fall patterns, enhancing prediction accuracy. The ensemble models also exhibit higher precision and recall rates, correctly identifying both hair fall and non-hair fall instances. The ensemble models' superiority stems from mitigating the limitations of individual algorithms, resulting in a comprehensive and robust prediction framework. Overall, this research showcases the efficacy of ensemble machine learning models in hair fall prediction, enabling early detection and intervention for hair loss prevention. These findings provide valuable insights for researchers, practitioners, and individuals concerned about hair health.