Building classifiers with GMDH for health social networks (DB AskaPatient)

In recent years., health social media has become a popular Internet resource., where various medicines and treatments are discussed and proposed. To make it useful for patients and doctors., different tools of opinion mining are developed and tested. In the paper., we study possibilities of Group Method of Data Handling (GMDH) to classify data from health social networks for the analysis of the most interesting cases for users. Here instead of usual 5-star classification., we use combined classes reflecting more practical view on medicines and treatments. The use of GMDH is provoked by two circumstances: (a) GMDH is essentially noise-immunity technology unlike the other ones used in Data Mining; b) GMDH-based classifiers use One-Vs-All approach being fit just for combined classes mentioned above. Our tool is the platform GMDH Shell including GMDH-based algorithms together with various procedures of pre-processing. The experimental material is the popular health social network AskaPatient. In the experiments, we use both the original and artificially noised data. The results prove to be promising in terms of its accuracy and stability.