The Predictive Model of Oral Squamous Cell Survival Carcinoma: A Methodology of Validation
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...
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| creator | Ahmad, Wan Muhamad Amir W Yaqoob, Muhammad Azeem Noor, Nor Farid Mohd Ghazali, Farah Muna Mohamad Rahman, Nuzlinda Abdul Tang, Liszen Aleng, Nor Azlida Alam, Mohammad Khursheed |
| description | 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 |
| doi_str_mv | 10.1155/2021/5436894 |
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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.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2021/5436894</identifier><identifier>PMID: 34904115</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Abnormalities ; Age ; Alcohol ; Cancer ; Care and treatment ; Cell survival ; Cell Survival - physiology ; Computer applications ; Datasets ; Diagnosis ; Epigenetics ; Error analysis ; Event history analysis ; Health hazards ; Human papillomavirus ; Humans ; Linear Models ; Lymphatic Metastasis - pathology ; Malaysia ; Mathematical models ; Medical prognosis ; Medical research ; Metastases ; Metastasis ; Methods ; Mortality ; Mouth cancer ; Mouth Neoplasms - mortality ; Mouth Neoplasms - pathology ; Multivariate Analysis ; Neoplasms ; Oral cancer ; Oral carcinoma ; Oral squamous cell carcinoma ; Patients ; Performance prediction ; Phenotypes ; Population ; Prediction models ; Prevention ; Prognosis ; Proportional Hazards Models ; Regression ; Regression analysis ; Retrospective Studies ; Risk Factors ; Smoking ; Sociodemographics ; Software ; Squamous cell carcinoma ; Squamous Cell Carcinoma of Head and Neck - mortality ; Squamous Cell Carcinoma of Head and Neck - pathology ; Statistical analysis ; Statistical models ; Statistical tests ; Survival ; Survival Rate ; Syntax ; Tumors</subject><ispartof>BioMed research international, 2021, Vol.2021 (1), p.5436894-5436894</ispartof><rights>Copyright © 2021 Wan Muhamad Amir W Ahmad et al.</rights><rights>COPYRIGHT 2021 John Wiley & Sons, Inc.</rights><rights>Copyright © 2021 Wan Muhamad Amir W Ahmad et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2021 Wan Muhamad Amir W Ahmad et al. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4272-3861d72511eeea3af711000ae38467bb632828062a378252ec1049c3a29699a03</citedby><cites>FETCH-LOGICAL-c4272-3861d72511eeea3af711000ae38467bb632828062a378252ec1049c3a29699a03</cites><orcidid>0000-0002-0573-3700 ; 0000-0003-2366-3918 ; 0000-0001-7131-1752</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665892/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665892/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,4010,27900,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34904115$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Kottayasamy Seenivasagam, Rajkumar</contributor><contributor>Rajkumar Kottayasamy Seenivasagam</contributor><creatorcontrib>Ahmad, Wan Muhamad Amir W</creatorcontrib><creatorcontrib>Yaqoob, Muhammad Azeem</creatorcontrib><creatorcontrib>Noor, Nor Farid Mohd</creatorcontrib><creatorcontrib>Ghazali, Farah Muna Mohamad</creatorcontrib><creatorcontrib>Rahman, Nuzlinda Abdul</creatorcontrib><creatorcontrib>Tang, Liszen</creatorcontrib><creatorcontrib>Aleng, Nor Azlida</creatorcontrib><creatorcontrib>Alam, Mohammad Khursheed</creatorcontrib><title>The Predictive Model of Oral Squamous Cell Survival Carcinoma: A Methodology of Validation</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>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.</description><subject>Abnormalities</subject><subject>Age</subject><subject>Alcohol</subject><subject>Cancer</subject><subject>Care and treatment</subject><subject>Cell survival</subject><subject>Cell Survival - physiology</subject><subject>Computer applications</subject><subject>Datasets</subject><subject>Diagnosis</subject><subject>Epigenetics</subject><subject>Error analysis</subject><subject>Event history analysis</subject><subject>Health hazards</subject><subject>Human papillomavirus</subject><subject>Humans</subject><subject>Linear Models</subject><subject>Lymphatic Metastasis - pathology</subject><subject>Malaysia</subject><subject>Mathematical models</subject><subject>Medical prognosis</subject><subject>Medical research</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>Methods</subject><subject>Mortality</subject><subject>Mouth cancer</subject><subject>Mouth Neoplasms - mortality</subject><subject>Mouth Neoplasms - pathology</subject><subject>Multivariate Analysis</subject><subject>Neoplasms</subject><subject>Oral cancer</subject><subject>Oral carcinoma</subject><subject>Oral squamous cell carcinoma</subject><subject>Patients</subject><subject>Performance prediction</subject><subject>Phenotypes</subject><subject>Population</subject><subject>Prediction models</subject><subject>Prevention</subject><subject>Prognosis</subject><subject>Proportional Hazards Models</subject><subject>Regression</subject><subject>Regression analysis</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>Smoking</subject><subject>Sociodemographics</subject><subject>Software</subject><subject>Squamous cell carcinoma</subject><subject>Squamous Cell Carcinoma of Head and Neck - mortality</subject><subject>Squamous Cell Carcinoma of Head and Neck - pathology</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Statistical tests</subject><subject>Survival</subject><subject>Survival Rate</subject><subject>Syntax</subject><subject>Tumors</subject><issn>2314-6133</issn><issn>2314-6141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kUtP3DAUha2qVUHAjjWK1E2lMuBXHLuLSqNReUggKpV2wca649zMGCUxOMlU_HsczXQoLOqNX5_PvceHkENGTxjL81NOOTvNpVDayHdklwsmJ4pJ9n67FmKHHHTdPU1DM0WN-kh2hDRUJoFdcne7xOxHxNK73q8wuw4l1lmospsIdfbzcYAmDF02wzrthrjyq3Q8g-h8Gxr4mk2za-yXoQx1WDyN735D7UvofWj3yYcK6g4PNvMe-XX2_XZ2Mbm6Ob-cTa8mTvKCT4RWrCx4zhgigoCqYCz1Cii0VMV8rgTXXFPFQRSa5xwdo9I4AdwoY4CKPfJtrfswzBssHbZ96t0-RN9AfLIBvH190_qlXYSV1Url2vAk8HkjEMPjgF1vG9-55BhaTOYtV2z8O0mLhH56g96HIbbJXqKoYTlXnL5QC6jR-rYKqa4bRe1Upa6V0mIse7ymXAxdF7HatsyoHdO1Y7p2k27Cj_61uYX_ZpmAL2tg6dsS_vj_yz0DvHioqw</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Ahmad, Wan Muhamad Amir W</creator><creator>Yaqoob, Muhammad Azeem</creator><creator>Noor, Nor Farid Mohd</creator><creator>Ghazali, Farah Muna Mohamad</creator><creator>Rahman, Nuzlinda Abdul</creator><creator>Tang, Liszen</creator><creator>Aleng, Nor Azlida</creator><creator>Alam, Mohammad Khursheed</creator><general>Hindawi</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7QO</scope><scope>7T7</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-0573-3700</orcidid><orcidid>https://orcid.org/0000-0003-2366-3918</orcidid><orcidid>https://orcid.org/0000-0001-7131-1752</orcidid></search><sort><creationdate>2021</creationdate><title>The Predictive Model of Oral Squamous Cell Survival Carcinoma: A Methodology of Validation</title><author>Ahmad, Wan Muhamad Amir W ; Yaqoob, Muhammad Azeem ; Noor, Nor Farid Mohd ; Ghazali, Farah Muna Mohamad ; Rahman, Nuzlinda Abdul ; Tang, Liszen ; Aleng, Nor Azlida ; Alam, Mohammad Khursheed</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4272-3861d72511eeea3af711000ae38467bb632828062a378252ec1049c3a29699a03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Abnormalities</topic><topic>Age</topic><topic>Alcohol</topic><topic>Cancer</topic><topic>Care and treatment</topic><topic>Cell survival</topic><topic>Cell Survival - physiology</topic><topic>Computer applications</topic><topic>Datasets</topic><topic>Diagnosis</topic><topic>Epigenetics</topic><topic>Error analysis</topic><topic>Event history analysis</topic><topic>Health hazards</topic><topic>Human papillomavirus</topic><topic>Humans</topic><topic>Linear Models</topic><topic>Lymphatic Metastasis - pathology</topic><topic>Malaysia</topic><topic>Mathematical models</topic><topic>Medical prognosis</topic><topic>Medical research</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>Methods</topic><topic>Mortality</topic><topic>Mouth cancer</topic><topic>Mouth Neoplasms - mortality</topic><topic>Mouth Neoplasms - pathology</topic><topic>Multivariate Analysis</topic><topic>Neoplasms</topic><topic>Oral cancer</topic><topic>Oral carcinoma</topic><topic>Oral squamous cell carcinoma</topic><topic>Patients</topic><topic>Performance prediction</topic><topic>Phenotypes</topic><topic>Population</topic><topic>Prediction models</topic><topic>Prevention</topic><topic>Prognosis</topic><topic>Proportional Hazards Models</topic><topic>Regression</topic><topic>Regression analysis</topic><topic>Retrospective Studies</topic><topic>Risk Factors</topic><topic>Smoking</topic><topic>Sociodemographics</topic><topic>Software</topic><topic>Squamous cell carcinoma</topic><topic>Squamous Cell Carcinoma of Head and Neck - mortality</topic><topic>Squamous Cell Carcinoma of Head and Neck - pathology</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Statistical tests</topic><topic>Survival</topic><topic>Survival Rate</topic><topic>Syntax</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ahmad, Wan Muhamad Amir W</creatorcontrib><creatorcontrib>Yaqoob, Muhammad Azeem</creatorcontrib><creatorcontrib>Noor, Nor Farid Mohd</creatorcontrib><creatorcontrib>Ghazali, Farah Muna Mohamad</creatorcontrib><creatorcontrib>Rahman, Nuzlinda Abdul</creatorcontrib><creatorcontrib>Tang, Liszen</creatorcontrib><creatorcontrib>Aleng, Nor Azlida</creatorcontrib><creatorcontrib>Alam, Mohammad Khursheed</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BioMed research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ahmad, Wan Muhamad Amir W</au><au>Yaqoob, Muhammad Azeem</au><au>Noor, Nor Farid Mohd</au><au>Ghazali, Farah Muna Mohamad</au><au>Rahman, Nuzlinda Abdul</au><au>Tang, Liszen</au><au>Aleng, Nor Azlida</au><au>Alam, Mohammad Khursheed</au><au>Kottayasamy Seenivasagam, Rajkumar</au><au>Rajkumar Kottayasamy Seenivasagam</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Predictive Model of Oral Squamous Cell Survival Carcinoma: A Methodology of Validation</atitle><jtitle>BioMed research international</jtitle><addtitle>Biomed Res Int</addtitle><date>2021</date><risdate>2021</risdate><volume>2021</volume><issue>1</issue><spage>5436894</spage><epage>5436894</epage><pages>5436894-5436894</pages><issn>2314-6133</issn><eissn>2314-6141</eissn><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.</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>34904115</pmid><doi>10.1155/2021/5436894</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-0573-3700</orcidid><orcidid>https://orcid.org/0000-0003-2366-3918</orcidid><orcidid>https://orcid.org/0000-0001-7131-1752</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2314-6133 |
| ispartof | BioMed research international, 2021, Vol.2021 (1), p.5436894-5436894 |
| issn | 2314-6133 2314-6141 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8665892 |
| source | MEDLINE; Wiley Online Library Open Access; PubMed Central; Alma/SFX Local Collection; PubMed Central Open Access |
| subjects | Abnormalities Age Alcohol Cancer Care and treatment Cell survival Cell Survival - physiology Computer applications Datasets Diagnosis Epigenetics Error analysis Event history analysis Health hazards Human papillomavirus Humans Linear Models Lymphatic Metastasis - pathology Malaysia Mathematical models Medical prognosis Medical research Metastases Metastasis Methods Mortality Mouth cancer Mouth Neoplasms - mortality Mouth Neoplasms - pathology Multivariate Analysis Neoplasms Oral cancer Oral carcinoma Oral squamous cell carcinoma Patients Performance prediction Phenotypes Population Prediction models Prevention Prognosis Proportional Hazards Models Regression Regression analysis Retrospective Studies Risk Factors Smoking Sociodemographics Software Squamous cell carcinoma Squamous Cell Carcinoma of Head and Neck - mortality Squamous Cell Carcinoma of Head and Neck - pathology Statistical analysis Statistical models Statistical tests Survival Survival Rate Syntax Tumors |
| title | The Predictive Model of Oral Squamous Cell Survival Carcinoma: A Methodology of Validation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T06%3A18%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20Predictive%20Model%20of%20Oral%20Squamous%20Cell%20Survival%20Carcinoma:%20A%20Methodology%20of%20Validation&rft.jtitle=BioMed%20research%20international&rft.au=Ahmad,%20Wan%20Muhamad%20Amir%20W&rft.date=2021&rft.volume=2021&rft.issue=1&rft.spage=5436894&rft.epage=5436894&rft.pages=5436894-5436894&rft.issn=2314-6133&rft.eissn=2314-6141&rft_id=info:doi/10.1155/2021/5436894&rft_dat=%3Cgale_pubme%3EA696966832%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2609152620&rft_id=info:pmid/34904115&rft_galeid=A696966832&rfr_iscdi=true |