In today’s highly competitive market, performing data analytics using machine learning (ML) models has become a necessity for organizations. It enables them to unlock the value of their data, identify trends, patterns, and predictions, and differentiate themselves from their competitors. For example, in the healthcare industry, ML-driven analytics can be used for diagnostic assistance and personalized medicine, while in health insurance, it can be used for predictive care management.
However, organizations and users in industries where there is potential health data, such as in healthcare or in health insurance, must prioritize protecting the privacy of people and comply with regulations. They are also facing challenges in using ML-driven analytics for an increasing number of use cases. These challenges include a limited number of data science experts, the complexity of ML, and the low volume of data due to restricted Protected Health Information (PHI) and infrastructure capacity.
Organizations in the healthcare,

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