This study found that machine learning techniques, such as Random Forest, SVMs, and KNN, can significantly improve the early detection and determination of hair loss, potentially transforming treatment with more accurate and personalized approaches compared to traditional methods.
September 2025 in “Matics Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology)” This study found that among various predictive models for baldness risk, Random Forest Regression performed best with the lowest mean squared error and highest R², indicating strong predictive accuracy, especially with complex datasets, while Linear Regression was better suited to simpler datasets.
June 2023 in “International journal on recent and innovation trends in computing and communication” This study found that ensemble machine learning models effectively predict hair fall by combining the strengths of individual algorithms, leading to higher accuracy, precision, and recall in identifying hair and non-hair fall instances compared to single algorithms.
7 citations
,
January 2012 This study used artificial neural networks to predict hair loss by analyzing factors like gender and zinc deficiency, suggesting neural networks may effectively model hair loss prediction.
In this study, machine learning-based computer-aided diagnosis significantly improved accuracy in diagnosing alopecia areata compared to traditional visual methods, achieving up to 91.9% accuracy using different classifiers like CNN, SVM, and random forest models.
