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Improving human health and providing access to high-quality healthcare for everyone is a global concern. Modern technologies help to promote and maintain health, while avoiding unnecessary disabilities and premature health issues. Machine learning is a subfield of artificial intelligence, which is mainly defined as the capability of a machine to imitate "intelligent" human behavior. This capacity is being widely applied in many areas of our lives such as virtual personal assistants, self-driving cars, security cameras, product recommendations, or disaster alerts. With the union of machine learning and healthcare, researchers around the world have opened new horizons providing impressive advances in healthcare. Thus, a great number of works focus on areas such as patient diagnosis, automating health-related tasks, new treatments and drugs, improvements in diagnosis, cost reduction, better tracking, or telemedicine. However, despite the huge amount of work carried out, we are still very far from being able to consider machine learning to be integrated into healthcare. The aim of this Reprint is to enhance the state-of-the-art in this area significantly, improving the application of machine learning techniques for healthcare.

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