## 9 Results and Experiments
### Case Study: Clinical Study on Patient Attributes  
This clinical study aims to see how PatientGender, PatientRace, PatientMaritalStatus, and PatientLanguage relate to each other through different patient attributes. To examine these relationships, we created three kinds of visualizations as follows:  
1. _Box plot:_ The patient age (used from PatientId) is displayed for different marital status categories in this visual. Age numbers are displayed on the \(y\)-axis, and marital statuses (including single, married, and divorced) are shown on the \(x\)-axis. Descriptive statistics, comprising the mean and median ages, are given for every marital status group, to highlight the overall trends. The age range for every marital status group is examined utilizing the following statistical measures (1), (2): \[\bullet\] Mean (m): \[\mu=\frac{1}{N}\sum_{i=1}^{N}x_{i}\] (1) where \(N\) is the number of patients in a marital status group, and \(x_{i}\) is the age of the \(i\)-th patient. \[\bullet\] Median (M): \[M=\left\{\begin{array}{ll}x\left(\frac{N+1}{2}\right),&if\ N\ is\ odd\\ \frac{x\left(\frac{N}{2}\right)^{+x}\left(\frac{N}{2}+1\right)}{2},&if\ N\ is\ even \end{array}\right.\] (2) where \(x(i)\) shows the \(i\)-th patient order statistic (i.e., the lowest value in the sorted data) [28]. 2. _Bar plot:_ This demonstrates how patients are divided by gender. Gender classifications (male and female) appear on the \(x\)-axis, while the number of patients is displayed on the \(y\)-axis. Every line shows the number of patients in every gender group. Distribution by gender can be obvious with the help of other statistics, like the percentage of male and female patients. The overall percentage indicates an average gender distribution, while the median number shows the central trend [29; 30]. The total number of patients by gender is determined as follows (3): \[\bullet\] Percentage (P): \[P=\left(\frac{n_{g}}{N}\right)\times 100\] (3)3. _Area plot_: It displays how patient language accessibility has evolved. The \(x\)-axis shows different periods (years, for example), as the \(y\)-axis represents the number of patients understanding each language. The plot's sections each indicate one language, while the interconnected parts show modifications to the distribution of every language. Those are the mean and median number of patients speaking each language for every observation period [31]. The transformation in language distribution is examined utilizing Formulas (4) and (5): \(\bullet\) The mean number of patients understanding a language (\(\mu\)L) during a period: \[\mu_{L  
Furthermore, taking advantage of three different visualization types--a box plot, a bar plot, and an area plot--and descriptive statistics, including the mean and median, we aim to investigate the data in-depth. Due to the proposed visualizations and descriptive statistics, we can identify interesting developments and trends in the data, including the age distribution of patients throughout marital status groups, the gender distribution of patients, and the change in language support over time. We might discover specific trends or challenges affecting how products and offerings are applied in the healthcare sector using these statistical methods. Also, by training elected officials, academics, and doctors, this research lets them make better choices and boosts patients' health.