Lung cancer detection using SVM algorithm / Nur Qamarina Ainaa Zulkifli
Lung cancer remains a significant global health challenge, with its prevalence escalating and posing a considerable threat to human life. Early detection plays a pivotal role in the effectiveness of treatment and patient prognosis. Lung tumors can be broadly categorized as either benign or malignant...
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| Language: | English |
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2024
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| Online Access: | https://ir.uitm.edu.my/id/eprint/96594/1/96594.pdf |
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my-uitm-ir.965942024-06-09T04:18:42Z Lung cancer detection using SVM algorithm / Nur Qamarina Ainaa Zulkifli 2024 Zulkifli, Nur Qamarina Ainaa Algorithms Lung cancer remains a significant global health challenge, with its prevalence escalating and posing a considerable threat to human life. Early detection plays a pivotal role in the effectiveness of treatment and patient prognosis. Lung tumors can be broadly categorized as either benign or malignant. It's important for individuals with lung nodules or suspected lung cancer to consult with healthcare professionals who can provide a thorough evaluation, accurate diagnosis, and appropriate treatment recommendations based on the specific circumstances of the case. This study has proposed a lung cancer detection model using support vector machine and a prototype was developed to detect whether it is cancerous or normal lung. The proposed model has achieved an accuracy percentage of lung cancer with 95.24%. The significance of this project is this prototype will give benefits to tall the medical officers in the hospital as they can check whether the patient has lung cancer or not. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96594/ https://ir.uitm.edu.my/id/eprint/96594/1/96594.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer and Mathematical Sciences Mohamad, Norizan |
| institution |
Universiti Teknologi MARA |
| collection |
UiTM Institutional Repository |
| language |
English |
| advisor |
Mohamad, Norizan |
| topic |
Algorithms |
| spellingShingle |
Algorithms Zulkifli, Nur Qamarina Ainaa Lung cancer detection using SVM algorithm / Nur Qamarina Ainaa Zulkifli |
| description |
Lung cancer remains a significant global health challenge, with its prevalence escalating and posing a considerable threat to human life. Early detection plays a pivotal role in the effectiveness of treatment and patient prognosis. Lung tumors can be broadly categorized as either benign or malignant. It's important for individuals with lung nodules or suspected lung cancer to consult with healthcare professionals who can provide a thorough evaluation, accurate diagnosis, and appropriate treatment recommendations based on the specific circumstances of the case. This study has proposed a lung cancer detection model using support vector machine and a prototype was developed to detect whether it is cancerous or normal lung. The proposed model has achieved an accuracy percentage of lung cancer with 95.24%. The significance of this project is this prototype will give benefits to tall the medical officers in the hospital as they can check whether the patient has lung cancer or not. |
| format |
Thesis |
| qualification_level |
Bachelor degree |
| author |
Zulkifli, Nur Qamarina Ainaa |
| author_facet |
Zulkifli, Nur Qamarina Ainaa |
| author_sort |
Zulkifli, Nur Qamarina Ainaa |
| title |
Lung cancer detection using SVM algorithm / Nur Qamarina Ainaa Zulkifli |
| title_short |
Lung cancer detection using SVM algorithm / Nur Qamarina Ainaa Zulkifli |
| title_full |
Lung cancer detection using SVM algorithm / Nur Qamarina Ainaa Zulkifli |
| title_fullStr |
Lung cancer detection using SVM algorithm / Nur Qamarina Ainaa Zulkifli |
| title_full_unstemmed |
Lung cancer detection using SVM algorithm / Nur Qamarina Ainaa Zulkifli |
| title_sort |
lung cancer detection using svm algorithm / nur qamarina ainaa zulkifli |
| granting_institution |
Universiti Teknologi MARA, Terengganu |
| granting_department |
Faculty of Computer and Mathematical Sciences |
| publishDate |
2024 |
| url |
https://ir.uitm.edu.my/id/eprint/96594/1/96594.pdf |
| _version_ |
1804889998019788800 |