Implementing Machine Learning Techniques for Hair Health Prediction in Follicle Futures

This study explores the effectiveness of various machine learning algorithms in forecasting hair health using a comprehensive dataset incorporating individual traits and lifestyle elements. Logistic regression, random forest, and AdaBoost were assessed, with random forest notably achieving a remarkable 94.6% accuracy. These results emphasize machine learning’s potential for precise, non-invasive personalized hair health predictions. Key factors such as genetics, diet, and stress were pinpointed, informing targeted care approaches. The research underscores machine learning’s promise in accurately anticipating hair loss and suggests future avenues for expanding datasets, integrating diverse data sources, and developing user-friendly tools for improved hair care management.