Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today's applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and applications equally efficient in the need of increasing data size and dimensionality [1]. To achieve this goal, many applications rely on parallelism, because it is an area that allows the reduction of cost depending on the execution time of the algorithms because it takes advantage of the characteristics of current computer architectures to run several processes concurrently [2]. This paper proposes a parallel version of the FuzzyPred algorithm based on the amount of data that can be processed within each of the processing threads, synchronously and independently.

In these times of pandemic, hospitals are being the focus of many innovations, not only for the adaptation to telemedicine, but also from the perspective of the use and processing of the multiple modalities of medical images, where we find images made up of a single Image such as x-rays, images that are made up of a sequence of images such as tomography and Magnetic Resonance, or in video format as is the case with ultrasound and angiography. One way of working with images is through popular image servers that connect to medical equipment for transfer and storage. In the process of visualization and processing, special workstations with good computational capacity are required for these purposes, in most cases these workstations are connected in the network of medical offices, therefore they are presented in a normal working image display requests at the same time. The methodology presen...

ACM-2022: Algorithms Computing and Mathematics Conference, August 29 – 30, 2022, Chennai, India.