Abstract
The enduring diagnosis of patient’s medical records might be useful to determine the causes that are responsible for a particular disease. So that, one can take early preventive measures to curtail the risk of diseases that may occur with the growing age. Consequently, this can enhance the life expectancy probability. Here, a new algorithm CMARM is proposed for analysis of symptoms in order to find out the disease that may occur frequently and rarely with growing age. It uses map reduce paradigm inspired by cognitive learning. It is concerned with acquisition of problem-solving skills, intelligence and conscious thought and uses prevailing knowledge to generate new rules. It has been evaluated over synthetic data sets collected from the health data repository. Since, CMARM requires one-time file access therefore, it is consistently faster and also consuming less memory space than the FP tree based algorithm.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
