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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Main Author: Zulkifli, Nur Qamarina Ainaa
Format: Thesis
Language:en
Published: 2024
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/96594/1/96594.pdf
https://ir.uitm.edu.my/id/eprint/96594/
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author Zulkifli, Nur Qamarina Ainaa
author_facet Zulkifli, Nur Qamarina Ainaa
author_sort Zulkifli, Nur Qamarina Ainaa
building Tun Abdul Razak Library
collection Institutional Repository
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
continent Asia
country Malaysia
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.
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institution Universiti Teknologi Mara
language en
publishDate 2024
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spelling my.uitm.ir-965942026-03-18T07:29:59Z https://ir.uitm.edu.my/id/eprint/96594/ Lung cancer detection using SVM algorithm / Nur Qamarina Ainaa Zulkifli 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 NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/96594/1/96594.pdf Zulkifli, Nur Qamarina Ainaa (2024) Lung cancer detection using SVM algorithm / Nur Qamarina Ainaa Zulkifli. (2024) Degree thesis, thesis, Universiti Teknologi MARA, Terengganu. <http://terminalib.uitm.edu.my/96594.pdf>
spellingShingle Algorithms
Zulkifli, Nur Qamarina Ainaa
Lung cancer detection using SVM algorithm / Nur Qamarina Ainaa Zulkifli
title 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_short Lung cancer detection using SVM algorithm / Nur Qamarina Ainaa Zulkifli
title_sort lung cancer detection using svm algorithm / nur qamarina ainaa zulkifli
topic Algorithms
url https://ir.uitm.edu.my/id/eprint/96594/1/96594.pdf
https://ir.uitm.edu.my/id/eprint/96594/
url_provider http://ir.uitm.edu.my/