The development of a new prognostic logistic regression model: a case study on oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is the sixth most frequent cancer worldwide. It is responsible for 80-90% of all mouth malignant neoplasms and has a mortality rate of up to 50%. Oral cancer has multifactorial etiology, mainly smoking, tobacco, alcohol consumption, betel quid chewing, and high...

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Main Author:	Azeem, Muhammad
Format:	Thesis
Language:	English
Published:	2023
Subjects:	R Medicine
Online Access:	http://eprints.usm.my/59894/1/MUHAMMAD%20AZEEM%20-FINAL%20THESIS%20PSGD001119%28R%29-E.pdf
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id	my-usm-ep.59894
record_format	uketd_dc
spelling	my-usm-ep.598942024-02-15T03:40:28Z The development of a new prognostic logistic regression model: a case study on oral squamous cell carcinoma 2023-07 Azeem, Muhammad R Medicine RC254-282 Neoplasms. Tumors. Oncology (including Cancer) Oral squamous cell carcinoma (OSCC) is the sixth most frequent cancer worldwide. It is responsible for 80-90% of all mouth malignant neoplasms and has a mortality rate of up to 50%. Oral cancer has multifactorial etiology, mainly smoking, tobacco, alcohol consumption, betel quid chewing, and high-risk human papillomavirus (HPV). A total of 57 patients were recruited from the clinic at the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling techniques were used to evaluate data descriptions on three risk factors studies: sociodemographic, clinicopathological, and surgical margins features of OSCC patients. The R-Studio software and syntax were used to design and develop the hybrid biometry method, implement, and the odd ratio. The advanced approach was executed in three parts, such as developing syntax for R for the biometry hybrid method which consists of data bootstrap methodology, multiple layer feedforward neural network (MLFFNN), and logistic regression. Male gender, smoking, betel quid, and alcohol habit variables were significantly related to death (p < 0.05). Among clinicopathological features increasing tumour size, metastasis, moderately and poorly differentiated OSCC, and Ki67 expression were significantly related to deceased patients (p < 0.05). Furthermore, features of surgical margins perineural invasion, bone invasion, and involvement of surgical margins were significantly related to the death of OSCC patients (p < 0.05). This finding might contribute to the underlying cause of poor prognosis. In conclusion, there exist potential risk factors in relation to OSCC in the Malaysian population. The conclusion of the study might illustrate the superiority of the hybrid model technique used in the study. 2023-07 Thesis http://eprints.usm.my/59894/ http://eprints.usm.my/59894/1/MUHAMMAD%20AZEEM%20-FINAL%20THESIS%20PSGD001119%28R%29-E.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Pergigian
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	R Medicine R Medicine
spellingShingle	R Medicine R Medicine Azeem, Muhammad The development of a new prognostic logistic regression model: a case study on oral squamous cell carcinoma
description	Oral squamous cell carcinoma (OSCC) is the sixth most frequent cancer worldwide. It is responsible for 80-90% of all mouth malignant neoplasms and has a mortality rate of up to 50%. Oral cancer has multifactorial etiology, mainly smoking, tobacco, alcohol consumption, betel quid chewing, and high-risk human papillomavirus (HPV). A total of 57 patients were recruited from the clinic at the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling techniques were used to evaluate data descriptions on three risk factors studies: sociodemographic, clinicopathological, and surgical margins features of OSCC patients. The R-Studio software and syntax were used to design and develop the hybrid biometry method, implement, and the odd ratio. The advanced approach was executed in three parts, such as developing syntax for R for the biometry hybrid method which consists of data bootstrap methodology, multiple layer feedforward neural network (MLFFNN), and logistic regression. Male gender, smoking, betel quid, and alcohol habit variables were significantly related to death (p < 0.05). Among clinicopathological features increasing tumour size, metastasis, moderately and poorly differentiated OSCC, and Ki67 expression were significantly related to deceased patients (p < 0.05). Furthermore, features of surgical margins perineural invasion, bone invasion, and involvement of surgical margins were significantly related to the death of OSCC patients (p < 0.05). This finding might contribute to the underlying cause of poor prognosis. In conclusion, there exist potential risk factors in relation to OSCC in the Malaysian population. The conclusion of the study might illustrate the superiority of the hybrid model technique used in the study.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Azeem, Muhammad
author_facet	Azeem, Muhammad
author_sort	Azeem, Muhammad
title	The development of a new prognostic logistic regression model: a case study on oral squamous cell carcinoma
title_short	The development of a new prognostic logistic regression model: a case study on oral squamous cell carcinoma
title_full	The development of a new prognostic logistic regression model: a case study on oral squamous cell carcinoma
title_fullStr	The development of a new prognostic logistic regression model: a case study on oral squamous cell carcinoma
title_full_unstemmed	The development of a new prognostic logistic regression model: a case study on oral squamous cell carcinoma
title_sort	development of a new prognostic logistic regression model: a case study on oral squamous cell carcinoma
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Pergigian
publishDate	2023
url	http://eprints.usm.my/59894/1/MUHAMMAD%20AZEEM%20-FINAL%20THESIS%20PSGD001119%28R%29-E.pdf
_version_	1794024067997630464

The development of a new prognostic logistic regression model: a case study on oral squamous cell carcinoma

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