Paper Key : IRJ************686
Author: Hemashree H,Anushka G,Sridevi N,Hasini R
Date Published: 17 Nov 2024
Abstract
Machine learning, particularly ensemble techniques, has gained prominence in medical diagnostics, including dermatology. Ensemble methods combine multiple algorithms to minimize bias and variance, enhancing the model's ability to generalize across diverse skin conditions. This is crucial in dermatology, where visual variations depend on demographics, skin tones, and environmental factors. By using techniques like bagging and boosting, ensemble methods address data imbalances and improve model robustness, making them suitable for real-world deployment.Such diagnostic tools align with the shift towards telemedicine and digital health. Integrating these systems into mobile or handheld devices allows users to access remote assessments, reducing the need for in-person consultations. This is particularly beneficial in low-resource areas where specialized care is limited. Early screening enabled by these tools can reduce delays, facilitate timely interventions, and potentially decrease disease progression risks.Beyond diagnostic accuracy, these systems can relieve healthcare pressure, especially in regions with high patient-to-dermatologist ratios. Future improvements could involve training on larger datasets and incorporating clinical data, such as patient history and genetics, to improve predictive accuracy. This technology could establish a new standard for accessible, proactive skin health management
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