Evaluation of Unsupervised Learning Modelling with Parallel Processes

Various medium and small enterprises in the healthcare industry, especially in the US and Canada foresee the growing demands of accurately classifying healthcare customers by integrating geographic, demographic and psychographic data with authoritative data from various sources in order to reduce costs from stakeholders (patients, healthcare service providers, and healthcare insurance companies). While the current classification algorithms can mostly detect and suggest meaningful patterns, they significantly consume resources (time and physical computational resources – processing, display, and memory). Therefore, this project aims to conduct various experiments to classify classes of skin cancer images by manipulating algorithms to consume the right balance of resources effectively.