## 4 Experimental Results and Discussion
### Statistical Analysis of Data
#### 4.1.4 Model 4: Estimation of the Recurrence Location Variable Using the Two-State Logit Model  
The estimation results of the two-state logit model for predicting the relapse location of patients are presented below. The mathematical model includes the dependent variable \"Recurrence Location\" and the independent variables that affect this variable.  
\(LocationofRecurrence=-0,0302+(0.0728*donorgendernale)+(0.0031* donorsize)+(0.0641*ty)+(0.0033* primarytunorsize)+(0.1959*numbertunors)-(0.0171*tumorsize2)+(0.0067*partum diameter2)\)  
The interpretation of the model will be based on the odds (OR) values. The results of the analysis revealed statistically significant relationships between various variables and tumor recurrence. Firstly, a statistically significant relationship was found between the donorboy variable and tumor recurrence. The odds ratio analysis showed that as the donorboy value increased, there was a decrease towards the patient tumor and recurrence category (OR = 0.993; \(p<0.01\)). Similarly, a statistically significant relationship was identified between the size of the anatomy and tumor recurrence. The odds ratio analysis indicated that as the size of the anatomy increased, there was an increase towards the patient tumor and recurrence category (OR = 1.003; \(p<0.01\)). Furthermore, a statistically significant relationship was observed between the number of tumors and tumor recurrence. It was found that as the number of tumors increased, there was an increase in the patient tumor and recurrence category (OR = 1.306; \(p<0.01\)). Additionally, a statistically significant relationship was found between the tumor diameter 2 variable and tumor recurrence. The odds ratio analysis showed that as the tumor diameter 2 variable increased, there was a decrease towards the patient tumor and recurrence category (OR = 0.975; \(p<0.01\)).  
Moreover, a statistically significant relationship was observed between the tumor localization 5 variable and tumor recurrence. The odds ratio analysis indicated that as the tumor localization 5 variable increased, there was a decrease towards the patient tumor and recurrence category (OR = 1.671; \(p<0.05\)). Similarly, a statistically significant relationship was found between the liver status variable and tumor recurrence. It was determined that as the liver status variable increased, there was a decrease towards the patient tumor and recurrence category (OR = 0.843; \(p<0.01\)). Furthermore, a statistically significant correlation was found between the pathology tumor diameter 1 variable and tumor recurrence. As the pathology tumor diameter 1 increased, there was a decrease towards the patient tumor and recurrence category (OR = 0.995; \(p<0.05\)). Similarly, a statistically significant correlation was observed between the pathology tumor diameter 2 variable and tumor recurrence. It was found that as the pathology tumor diameter 2 increased, there was an increase towards the patient tumor and recurrence category (OR = 1.007; \(p<0.05\)). Lastly, a statistically significant relationship was found between the Ht variable and tumor recurrence. The odds ratio analysis showed that as Ht increased, there was an increase towards the patient tumor and recurrence category (OR = 2.210; \(p<0.01\)).