Materials and Methods: At the first stage, all the selected variables will be a screen for their clinical important point of view, and it was found that creatinine has a significant relationship to the level of urea reading, a total of cholesterol reading, and the level of uric acid reading. By considering the level of significance, α = 0.05, these three variables are being selected and used for the input of the MLP model. Then, the MLR is being applied according to the best variable obtained through MLP process.

Results: Through the testing/out-sample mean squared error (MSE), the performance of MLP was assessed. MSE is an indication of the distance from the actual findings from our estimates. The smallest MSE of the MLP shows the best variable selection combination in the model.

Objective: To determine the additional relationship between factors discovered by searching for sociodemographic and metastasis factors, as well as treatment outcomes, which could help improve the prediction of the survival rate in cancer patients. Material and Methods. A total of 56 patients were recruited from the ambulatory clinic at the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling techniques were used to evaluate data descriptions of several variables such as treatment, age, and distant metastasis. The R-Studio software and syntax were used to implement and test the hazard ratio. The statistics for each sample were calculated using a combination model that included methods such as bootstrap and multiple linear regression (MLR).

Results: The statistical strategy showed R demonstrates that regression modeling outperforms an R-squared. It demonstrated that when data is partitioned into a training and testing dataset, the hybrid model technique performs better at predicting the outcome. The variable validation was determined using the well-established bootstrap-integrated MLR technique. In this case, three variables are considered: age, treatment, and distant metastases. It is important to note that three things affect the hazard ratio: age (β 1: -0.006423; p < 2e - 16), treatment (β 2: -0.355389; p < 2e - 16), and distant metastasis (β 3: -0.355389; p < 2e - 16). There is a 0.003469102 MSE for the linear model in this scenario.

MATERIALS AND METHODS: This chapter summarizes the medical history for 7 years from January 2011 to December 2018 of patients who have been treated for oral carcinoma in the Hospital USM, Oral and Maxillofacial Surgery (OMFS) Unit. Each patient's complete medical record was checked, and data gathered were based on age, gender, site lesion, clinical diagnosis, and mortality. Version 26.0 of the SPSS software was used to evaluate the correlation and distribution of patient survival.

RESULTS: This was a retrospective cross-sectional review of the medical evidence of 117 patients infected for oral carcinoma at OMFS (Hospital USM). Sixty-seven (57.26%) of the patients were male and fifty (42.74%) were female. Patient age ranged from 25 to 93 years. Malay has the highest prevalence (85.5%) in oral carcinoma, followed by a second ethnic group, Chinese (7.7%). The result indicates that the majority of oral carcinoma patients were over 60 years old.Cases of oral squamous cell carcinoma have proved to be the most prevalent malignant tumour in the mouth cavity. The largest number of cases collected is 91% of the data collected. Mucoepidermoid carcinoma (10%) is the second most common small salivary gland tumor.