Disease Diagnosis Supported by Hierarchical Temporal Memory

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.