Learning and Sequential Decision Making For Medical Data Streams Using Rl Algorithm


Data stream mining has obtained a high attraction due to the importance of its applications and increase in the generation of streaming information. The data streams are the set of data, which are moving in specified distribution. Extracting knowledge structures represented in models and patterns in non stopping streams of information is known as data stream mining. Increasing access to incredibly large, non stationary data sets and corresponding demands to analyze these data has led to use reinforcement learning algorithm on data streams. For diabetes data set it is difficult to differentiate between the value of blood glucose level and value of insulin doses. Also it is difficult to take decision about giving specific quantity of insulin dose to the diabetes patient. In this paper we implement the reinforcement learning algorithm on diabetes data streams. The algorithm process the data streams and differentiate the values for blood glucose level and insulin dose and take the decision for next insulin dose. Depending on state and action taken the payoff is assign to the decision. This helps in classifying the data for diabetes doses and also helps in making decisions for giving specific quantity of dose at a particular time. In comparison with other methods our proposed algorithm is faster. A proposed methodology is tested on diabetes data.

Authors and Affiliations

Prof. Pramod Patil, Dr. Parag Kulkarni, Ms. Rachana Shirsath


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  • EP ID EP27602
  • DOI -
  • Views 216
  • Downloads 3

How To Cite

Prof. Pramod Patil, Dr. Parag Kulkarni, Ms. Rachana Shirsath (2013). Learning and Sequential Decision Making For Medical Data Streams Using Rl Algorithm. International Journal of Research in Computer and Communication Technology, 2(7), -. https://europub.co.uk/articles/-A-27602