Non Small Cell Lung Cancer Diagnosis Using kNN and Logistic Regression

One in nine people develop cancer during their life time in India. Chances are that we already know someone who has been affected by cancer. But what is cancer? Simply put, they are made of trillions of cells that grow and divide over one's lifetime as needed. Abnormal or old cells die after a...

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Bibliographic Details
Published in2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) pp. 1 - 7
Main Authors S P, Siddique Ibrahim, C, Bharathi Priya, S, Selva Kumar, S, Vinoth Kumar
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.07.2023
Subjects
Cancer
CT images
Gene mutation
Logistic regression
Lung cancer
Lung cancer detection
machine learning
Machine learning algorithms
Medical diagnostic imaging
Medical services
Tumors
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Summary:One in nine people develop cancer during their life time in India. Chances are that we already know someone who has been affected by cancer. But what is cancer? Simply put, they are made of trillions of cells that grow and divide over one's lifetime as needed. Abnormal or old cells die after a certain period of time but some cells do not die and overcrowd the space and stop the body from functioning normally. The most recent screening technology makes early identification of lung cancer simple and may extend the patient's life. The use of automated technologies can also improve the accuracy of illness detection, aiding medical professionals in making accurate diagnoses. The automated lung cancer diagnosis method described in this article uses a machine learning algorithm to distinguish between benign, malignant, and normal lung cancer. The proposed lung cancer detection technique has a 98.7% accuracy rate, which is higher than the approaches that were examined.
ISSN:2473-7674
DOI:10.1109/ICCCNT56998.2023.10307547