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A Big Data Analytical Approach for Prediction of Cancer Using Modified K-Nearest Neighbour Algorithm

Today, sensors generate vast amounts of data in different fields such as hospitals, the transport sector, social media, and so on. In hospitals, the use of sensors that are installed in the patient’s body to monitor the pulse rate, heartbeats, head movement, eyes, and other body
 parts. Every day, these collected data are stored in local data servers and database servers by various sensors that require effective handling of these data. Sensors are primarily used in most of the IoT applications in everyday life from which smart city plays a crucial role. The aim of
 the work is to address the application of big data in healthcare and life science, including different types of data that involve special attention in processing. This work focuses on the use of large-data analytical techniques to process medical data. A large volume of unstructured cancer
 database is considered to identify and predict different types of cancer such as breast cancer, lung cancer, blood cancer, and so forth. This research involves the segmentation of thousands of records on cancer forms in a broad cancer database into various segmented databases. Using KNN algorithm
 this segmentation, classification and prediction will be achieved.