An Early Hair Loss Detection And Prediction Method Based On Machine Learning

The study investigates the use of machine learning for early detection and prediction of hair loss, utilizing algorithms such as Random Forest, which achieved a high accuracy of 99.80%. Conducted on a dataset of 999 entries with 13 attributes, the research underscores the role of genetics and lifestyle in hair loss. It suggests that machine learning can improve diagnostic accuracy and patient outcomes by offering more targeted therapies. The study also notes the need for further optimization to address sensitivity and learning bias, with future work focusing on expanding datasets and refining predictive capabilities to enhance diagnostic tools in trichology.