## 3 Experiments and Results
### The PubMed Case Study  
The Experiment section discussed above has shown the new methods and approaches this research have taken to produce a label for never-before-seen news articles. However, the premise of the algorithm was to show that it could work the same way with other textual inputs, such as medical publications, doctors' notes, etc. In this section we demonstrate another case scenario that will show how NeoNet is a text-classification general purpose algorithm that can perform the same way, regardless of the input source type (news, online medical forums, doctors' notes, or medical publication). Starting with a publication dataset extracted from PubMed, after searching the web portal for keywords such as (COVID-19 and coronavirus). Although, the search results produced more than 2500 medical abstracts (approx. 100, 000), we only used 500 abstracts for training, and 5-fold of 500 abstracts for testing. This was indeed consistent with the same experiments performed against the CBC news dataset.  
Following the same processes explained above and also demonstrated in Figure 1, we extracted the TF-IDF bigrams and constructed a similar training network model. The  
Figure 6: Shows the performance analysis of NeoNet vs. the most prominent Machine Learning algorithms and other common statistical-based methods.  
generated network constructed had the following characteristics: (1) number of nodes: 467, (2) number of edges: 330, and (3) average degree: 2.83. Table 2 below draws a comparison between the two training models generated from the two datasets. While the two datasets produced a relatively close number of nodes, the PubMed dataset had a significantly smaller number of edges. This is explained by the fact that publications covered various \"clusters\" of public health issues, such as the vaccine development, drug treatment, the COVID-19 disease, and its impact on the human body, among other things. On the other hand, news articles addressed the general public in much less domain-specific but commonly related terms.  
As for the actual classification results, we performed similar experiments against the 5-fold test sets provided by the PubMed dataset. Each fold was tested using a minimum support of the following values [5; 10; 15; 20; 25]. We observed a very similar pattern: the more the number of bigrams needed to classify a document as a COVID-19 article, the lower the precision of the classification. For example, when the classification required 5-bigrams (i.e., 10 connected terms in the training model) the classification precision fluctuated between 98-99.4%. In another case, when the minimum support was set to 25-bigrams (i.e., 50 connected terms in the training model), the classification precision fluctuated between 89.98-91.98%.  
Figure 7 shows the entire analysis of each fold and precision resulting from the various minimum support configurations. When comparing the precision derived from the 5-fold CBC News dataset, we found that precision fluctuated between 99.0-100% when the minimum support was set to 5-bigrams. When the minimum support was set to 25-bigrams, the precision fluctuated between 94.18-95.59%. Clearly, the drop-in precision in the PubMed 5-folds was due the training model being less connected, due to the significantly lower number of edges in its training model. We believe the precision could be enhanced in the case if multilabel classification (vaccine development, drug treatment, and COVID-19 disease and symptoms, etc.) This finding requires further investigation in the future to assess how the training model can offer more insights using the underlying inherent subtopics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DataSet** & **Nodes** & **Edges** & **AVG Degree** \\ \hline CBC News & 412 & 471 & 2.28 \\ PubMed & 467 & 330 & 2.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Describes the structural properties of the network training models generated from two different datasets.