## 3 Materials and Methods
\begin{table}
\begin{tabular}{c c c c} \hline
**Item Sets** & **Support** & **Confidence** & **Lift** \\ \hline
[”anitretroviral”, “hiv”] & 0.013036 & 0.705882 & 12.866627 \\
[”biopsy”, “patient”] & 0.024986 & 0.511112 & 4.376537 \\
[”cell”, “wall”] & 0.025529 & 0.72307 & 12.558345 \\
[”fluid”, “meningitis”] & 0.008691 & 0.551724 & 18.809706 \\
[”hiv”, “virus”] & 0.012493 & 0.511112 & 14.167360 \\
[”imaging”, “resonance”] & 0.009234 & 0.67999 & 27.81955 \\
[”kag\", “mutations”] & 0.017381 & 0.680851 & 11.292313 \\
[”lymph\", “nodes”] & 0.016295 & 0.967741 & 32.992831 \\ \hline \end{tabular}
\end{table} 
Table 5: Sample item sets of ethambutol dataset. ### Filtering Relation Records Based on Maximum Co-Occurrence Value and Set Intersection Property

The generated JSON files consist of the relation records. These records are filtered to determine the most weighted relationships. The python sets are used for this purpose. The set intersection is applied among the records, to group similar relations and to obtain unique inferences at the end. Then, the relation with maximum co-occurrence value from each group of records is extracted and added to the resultant set. Therefore, eight different filtered resultant sets, each corresponding to a drug, are obtained through this method. Figure 3 shows the resultant sets obtained for eight different TB drugs with co-occurrence value corresponding to each item of each set. The records present in the resultant set provide some important information about the drug. For example, in Figure 3c, ['genes', 'inha', 'katg'] is one of the elements of the resultant set obtained for the isoniaid dataset. The following inference is obtained for this record. _M.tb_ poses a great challenge at the scientific level as it acquires gene mutations, which develop resistance to the drugs and treatment forms. The mutations at codon 315 (amino acid position) of the katG gene are associated with high resistance to isoniaid. Therefore, isoniaid is ineffective in the treatment of _M.tb_ with this mutation [30]. Whereas the inhA gene mutations are associated with low-level resistance to isoniaid and thus high doses of the drug can be used for _M.tb_ treatment with this mutation [31]. Similarly, various inferences can be obtained from the resultant sets of 8 different TB drugs. Thus, the filtered resultant sets provide the most essential and unique inferences of the corresponding dataset. Figure 3: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item sets** & **Support** & **Confidence** & **Lift** \\ \hline
[\"activity\", \"bactericidal\"] & 0.010021 & 0.5 & 8.697247 \\
[\"care\", \"hiv\"] & 0.018459 & 0.5 & 6.236842 \\
[\"chest\", \"x-ray\"] & 0.008438 & 0.516129 & 17.168081 \\
[\"drug-induced\", \"injury\"] & 0.013713 & 0.541667 & 18.017543 \\
[\"fluoroquinolone\", \"resistance\"] & 0.009493 & 0.545454 & 3.262403 \\
[\"hepatic\", \"liver\"] & 0.010548 & 0.606061 & 13.207941 \\
[\"interferon-gamma\", \"release\"] & 0.009493 & 0.9 & 33.458823 \\
[\"nontuberculosis\", \"ntm\"] & 0.008438 & 0.666666 & 48.615384 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Sample item sets of isoniaid dataset. Figure 3: _Cont_. Since the relation records are generated using affinity analysis, they do not contain much noise. The resultant set is obtained through set intersection property and hence, there will not be any repetition of records. The obtained resultant set contains various inferences. These inferences can help the medical experts in their research and can also lead to some discoveries. Suppose if this method is applied on different disease datasets, we may extract some common features and relationships between the diseases. In this paper, the resultant sets of eight different TB drugs are analyzed and compared, to discover such inferences. Though these drugs are developed to work against the pathogens causing TB, there are chances that they might work against other infections. Extracting such inferences may help in the development of new treatments for some diseases.