Abstract
Background Cancer is primarily caused by smoking, alcohol, betel quit, a series of genetic alterations, and epigenetic abnormalities in signaling pathways, which result in a variety of phenotypes that favor the development of OSCC. Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer, accounting for 80–90% of all oral malignant neoplasms. Oral cancer is relatively common, and it is frequently curable when detected and treated early enough. The tumor-node-metastasis (TNM) staging system is used to determine patient prognosis; however, geographical inaccuracies frequently occur, affecting management. 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 (β1: -0.006423; p < 2e − 16), treatment (β2: -0.355389; p < 2e − 16), and distant metastasis (β3: -0.355389; p < 2e − 16). There is a 0.003469102 MSE for the linear model in this scenario. Conclusion In this study, a hybrid approach combining bootstrapping and multiple linear regression will be developed and extensively tested. The R syntax for this methodology was designed to ensure that the researcher completely understood the illustration. In this case, a hybrid model demonstrates how this critical conclusion enables us to better understand the utility and relative contribution of the hybrid method to the outcome. The statistical technique used in this study, R, demonstrates that regression modeling outperforms R-squared values of 0.9014 and 0.00882 for the predicted mean squared error, respectively. The conclusion of the study establishes the superiority of the hybrid model technique used in the study.
Highlights
Oral squamous cell carcinomas (OSCC) are the sixth most common malignant tumor [1], and they are a fatal oral cavity disease that accounts for up to 50% of all deaths and multiple factors playing a role in survival rate such as T4 stage diagnosis and late age [2]
The bootstrap method generates a sample of the same size as the original sample, but with each observation repeated several times and others discarded [18, 19]. This current study is aimed at investigating the performance of the multiple linear regression (MLR) using the recently created approach, which takes into account both the training and testing datasets
The hazard ratio has been significantly influenced by three factors: age (β1: -0.006423; p < 2e − 16), treatment (β2: -0.355389; p < 2e − 16), and distant metastasis (β3: -0.355389; p < 2e − 16)
Summary
Oral squamous cell carcinomas (OSCC) are the sixth most common malignant tumor [1], and they are a fatal oral cavity disease that accounts for up to 50% of all deaths and multiple factors playing a role in survival rate such as T4 stage diagnosis and late age [2]. 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 syntax for this methodology was designed to ensure that the researcher completely understood the illustration In this case, a hybrid model demonstrates how this critical conclusion enables us to better understand the utility and relative contribution of the hybrid method to the outcome. The conclusion of the study establishes the superiority of the hybrid model technique used in the study
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