Abstract
Hierarchical Temporal Memory (HTM) is an advanced machine learning technique that aims to capture the structural and algorithmic properties of the neocortex. It is an online machine learning method and can make predictions and classifications. This paper is an application of the HTM theory. We use the HTM theory to diagnose diseases. First, we encode the cases of the patients into the format that can be recognized by the HTM. Second, we train the HTM by using the encoded cases. At last, we can predict the disease of a patient according to the symptoms. We do experiments on real nephrosis diagnostic datasets. The experimental results show that we can predict the diseases accurately and efficiently.
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.
