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Item Skin Cancer Awareness and Detection(Clayton State University) Nnoroum, Godstime; Akhtar, Shakil; Nyguen , Ken; Rahman , Muhammad; Department of Computer Science and Information TechnologyThe rapid increase in digital health data has led to new areas of research in healthcare and data sciences. Traditional methods of handling health data have struggled because they can't manage the huge, fast-moving, and diverse amounts of data that are constantly changing. Skin cancer, particularly melanoma, represents a significant public health challenge due to factors such as increased ultraviolet (UV) radiation exposure and evolving lifestyle patterns. The application of big data and machine learning technologies offers promising advancements in the early detection of skin cancer by processing and analyzing extensive datasets, which include patient histories, environmental exposures, and genetic predispositions. Machine learning algorithms, particularly those focused on dermatological image recognition, enable the identification of skin lesions with high precision, thus facilitating the timely diagnosis of melanoma. Furthermore, predictive analytics models can identify individuals at heightened risk, potentially enabling early interventions and more personalized preventive strategies. The integration of big data and advanced computational techniques into skin cancer detection holds the potential to significantly enhance early diagnosis, treatment outcomes, and overall prevention efforts. The software using the latest machine learning techniques is under development at the CS/IT Department at Clayton State University as a funded NSF project. The purpose of this thesis is to collect, tabulate, and analyze skin cancer detection data among various segments of population. In addition to the initial testing and detection is carried out by using the software smartphone application software are developed for Android and Mac platforms. The apps allow for secure and private upload of images predicting various types of skin cancer with a percentage likelihood.