Unmasking Hair Loss Through a Fusion of Human Lifestyle Data Using Machine Learning Algorithms

Hair loss is a prevalent problem that is impacted by genetics, lifestyle, and medical factors. Prediction accuracy is decreased by traditional studies' frequent reliance on small datasets. To determine the main causes of hair loss, this study combines real-time data with machine learning algorithms like Random Forest, Decision Tree, XGBoost, Extra Trees, and Ensemble approaches. To improve feature selection and model performance, we use Generative Adversarial Networks (GANs) for augmentation and SMOTE for data balance. Our method facilitates early detection, increases predictive accuracy, and permits tailored therapy suggestions. This study offers practical advice that enables people to properly manage the health of their hair.