Lung cancer prediction using machine learning techniques / Muhammad Muhaimin Mohd Fauzi and Mohd Nizam Osman
Lung, prostate, and colorectal cancers are responsible for up to 45 percent of all cancer-related deaths. Therefore, it is of the biggest significance to recognise or predict it prior to its crucial stages. It is possible to save lives by identifying and treating cancer in its earliest stages. The c...
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College of Computing, Informatics and Media, UiTM Perlis
2023
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my.uitm.ir.1005272024-09-27T08:44:54Z https://ir.uitm.edu.my/id/eprint/100527/ Lung cancer prediction using machine learning techniques / Muhammad Muhaimin Mohd Fauzi and Mohd Nizam Osman Mohd Fauzi, Muhammad Muhaimin Osman, Mohd Nizam Machine learning Lung, prostate, and colorectal cancers are responsible for up to 45 percent of all cancer-related deaths. Therefore, it is of the biggest significance to recognise or predict it prior to its crucial stages. It is possible to save lives by identifying and treating cancer in its earliest stages. The classification of cancer risks, such as high risk and low risk, is often achieved using statistical methods. When this occurs, it may be very difficult to handle the complex interactions of high-dimensional data. To bypass these limitations, which are caused by the vast size of the data, techniques from the area of machine learning may be used. Therefore, for the aim of this research, machine learning techniques such as K-nearest neighbours, decision tree, and logistic regression were employed to predict the probability of developing cancer. College of Computing, Informatics and Media, UiTM Perlis 2023 Book Section PeerReviewed text en https://ir.uitm.edu.my/id/eprint/100527/1/100527.pdf Lung cancer prediction using machine learning techniques / Muhammad Muhaimin Mohd Fauzi and Mohd Nizam Osman. (2023) In: Research Exhibition in Mathematics and Computer Sciences (REMACS 5.0). College of Computing, Informatics and Media, UiTM Perlis, pp. 87-88. ISBN 978-629-97934-0-3 |
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Machine learning Mohd Fauzi, Muhammad Muhaimin Osman, Mohd Nizam Lung cancer prediction using machine learning techniques / Muhammad Muhaimin Mohd Fauzi and Mohd Nizam Osman |
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Lung, prostate, and colorectal cancers are responsible for up to 45 percent of all cancer-related deaths. Therefore, it is of the biggest significance to recognise or predict it prior to its crucial stages. It is possible to save lives by identifying and treating cancer in its earliest stages. The classification of cancer risks, such as high risk and low risk, is often achieved using statistical methods. When this occurs, it may be very difficult to handle the complex interactions of high-dimensional data. To bypass these limitations, which are caused by the vast size of the data, techniques from the area of machine learning may be used. Therefore, for the aim of this research, machine learning techniques such as K-nearest neighbours, decision tree, and logistic regression were employed to predict the probability of developing cancer. |
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Book Section |
author |
Mohd Fauzi, Muhammad Muhaimin Osman, Mohd Nizam |
author_facet |
Mohd Fauzi, Muhammad Muhaimin Osman, Mohd Nizam |
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Mohd Fauzi, Muhammad Muhaimin |
title |
Lung cancer prediction using machine learning techniques / Muhammad Muhaimin Mohd Fauzi and Mohd Nizam Osman |
title_short |
Lung cancer prediction using machine learning techniques / Muhammad Muhaimin Mohd Fauzi and Mohd Nizam Osman |
title_full |
Lung cancer prediction using machine learning techniques / Muhammad Muhaimin Mohd Fauzi and Mohd Nizam Osman |
title_fullStr |
Lung cancer prediction using machine learning techniques / Muhammad Muhaimin Mohd Fauzi and Mohd Nizam Osman |
title_full_unstemmed |
Lung cancer prediction using machine learning techniques / Muhammad Muhaimin Mohd Fauzi and Mohd Nizam Osman |
title_sort |
lung cancer prediction using machine learning techniques / muhammad muhaimin mohd fauzi and mohd nizam osman |
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College of Computing, Informatics and Media, UiTM Perlis |
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2023 |
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https://ir.uitm.edu.my/id/eprint/100527/1/100527.pdf https://ir.uitm.edu.my/id/eprint/100527/ |
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