# Tabular Data Generation to Improve Classification of Liver Disease Diagnosis
## 3 I.L.P.D. Dataset: Exploratory Data Analysis
### Exploratory Data Analysis  
Most of the results we derived from this analysis confirm the research and other resources available ([PERSON] website Indian liver disease analysis); such patterns must be confirmed in several sets to be beneficial for data understanding. Furthermore, more data allows us to zoom in on multiple facets in the data. In the analysis, E.D.A. is only a tool that gives the analyst insight into the nature of the data since it includes the word <<explorational>>. There are stronger statistical procedures such as modelling and inference, which can help us make better use of data and draw more solid conclusions. We have found that the results of blood tests may vary between men and women with liver disease. Without solid evidence, the conclusion could be reached that men are more severely affected by liver disease than women or that there is a sexual confounder (e.g., physiological differences or risk factors of the behavior) which causes the difference. On the other hand, gender does not seem to influence the presence or lack of liver disease, i.e., every gender has \"shared\" relatively positive and negative cases. It is worth noting, however, that the average age diagnosed as positive is higher in both women and men than the average age diagnosed as negative. A matrix for correlation, as shown in Figure 1, can be an extremely concise way of examining links between numerical variables. Some of the surprisingly strong correlations between ALT (both liver enzymes), AST and total protein (the former is only the most plentiful), and albumin (being a protein in abundance) are direct bilirubin with total proteins. This is also high and not as large as other strong correlations because globulins introduce some variability into the ratio, which is not shared with albumin. The ratio is not as large. Other strong correlations exist between direct bilirubin and total bilirubin (as the former is only a component of the latter) and between AST and ALT (both liver enzymes) and albumin (which is the most abundant protein). This is also high and not as large as other strong correlations because globulins introduce some variability into the ratio that is not shared with albumin, as seen from the pairplot for clarification in Figures 2 and 3. \begin{table}
\begin{tabular}{c c c c} \hline
**Sl. No** & **Attribute Name** & **Attribute Type** & **Attribute Description** \\ \hline \(1\). & Age & Numeric & Age of the patient \\ \hline \(2\). & Sex & Nominal & Gender of the patient \\ \hline \(3\). & Total Bilirubin & Numeric & Quantity of total bilirubin in patient \\ \hline \(4\). & Direct Bilirubin & Numeric & Quantity of direct bilirubin in patient \\ \hline \(5\). & Alkphos Alkaline & & \\ \(5\). & Phosphatase & Numeric & Amount of A.L.P. enzyme in patient \\ \hline \(6\). & Sgpt Alamine & & \\ \(7\). & Aminotransferase & Numeric & Amount of S.G.P.T. in patient \\ \hline \(7\). & Sgpt Aspartate & & \\ \(7\). & Aminotransferase & Numeric & Amount of S.G.O.T. in patient \\ \hline \(8\). & Total Proteins & Numeric & Protein content in patient \\ \hline \(9\). & Albumin & Numeric & Amount of albumin in patient \\ \hline \(10\). & Albumin and & & Fraction of albumin and globulin in Patient \\ \(11\). & Class & Numeric [1,2] & Status of liver disease in patient \\ \hline \end{tabular}
\end{table} 
Table 1: Description of Liver patient dataset. Figure 1: Simple Correlation Plot- liver disease. Figure 2: Pair Plot- liver disease. A pair-plot is a graphical tool that allows visualizing the pairwise relationships between multiple variables in a dataset. Each variable is plotted against all the other variables in the dataset, resulting in a grid of scatterplots. A pair plot can help identify patterns and trends in the relationships between variables and is useful for exploring the structure of the data, as we can see in in Figure 2.  
On the other hand, a pair plot with label categories Figure 3 is a variation of the basic pair plot that includes information about label categories.