Authors :

Mustafa Ali Hameed

Address :

Al-Farahidi University Baghdad, Iraq Mah625142@yol.com

Abstract :

Concerning big data in healthcare, PrecisionSkin offers a novel method for improving skin cancer stage classification. By combining Rough Set Theory (RST) with extensive healthcare datasets, the study proposes a PrecisionSkin-RST framework that creates a robust framework for precisely classifying skin cancer stages, crucial for timely treatment and diagnosis planning. PrecisionSkin significantly enhances the accuracy of stage categorization in medical data, addressing the challenges of imprecision and uncertainty. This breakthrough can lead to transformative improvements in patient treatment. An assessment was carried out on PrecisionSkin using various healthcare data, like medical records, medical imaging, and pathology reports. The findings suggest a significant enhancement in classification precision, with an average rise of 12% compared to conventional approaches. PrecisionSkin obtained an average accuracy of 97.8% and precision of 92% across diverse skin cancer types, surpassing previous approaches by a significant amount. Moreover, the suggested approach demonstrates resilience in managing extensive datasets, displaying effective processing times suitable for real-time clinical uses. The improved classification accuracy of PrecisionSkin has the potential to revolutionize skin cancer management, making it easier to develop individualized treatment plans and significantly boost patient outcomes. These findings underscore the effectiveness and potential of using the Rough Set Theory in large-scale healthcare to enhance skin cancer detection and therapy.

Keywords :

PrecisionSkin; Big Data Healthcare; Skin Cancer Classification; Rough Set Theory; Classification Accuracy; Precision Improvement; Processing Efficiency.