Diagnosis and Prognosis of Lung Cancer & Lung Nodule using Machine Learning Techniques

Sangeeta Devi; Pranjal Maurya; Munish Saran; Rajan Kumar; Upendra Nath Tripathi

doi:10.36948/ijfmr.2024.v06i01.12760

Diagnosis and Prognosis of Lung Cancer & Lung Nodule using Machine Learning Techniques

Author(s)	Sangeeta Devi, Pranjal Maurya, Munish Saran, Rajan Kumar, Upendra Nath Tripathi
Country	India
Abstract	Lung cancer is a serious and challenging cancer to diagnose. It frequently results in death in both men and women; thus, prompt, precise nodule analysis is crucial to the course of treatment. Early cancer detection has been accomplished through a variety of techniques. This research compares machine learning techniques for lung cancer nodule detection. To find anomalies, we used machine learning techniques such as principal component analysis, K-nearest neighbors, support vector machines, Naïve Bayes, decision trees, and artificial neural networks. We examined every technique with and without preprocessing. According to the experimental results, decision trees produce the most accurate results with 93,24% effectiveness without image processing while artificial neural networks produce the finest results with 82,43% effectiveness after image processing.
Keywords	lung cancer, decision tree, artificial neural networks, Naïve Bayes, classification, machine learning, support vector machine and diagnosis.
Field	Computer Applications
Published In	Volume 6, Issue 1, January-February 2024
Published On	2024-02-11
Cite This	Diagnosis and Prognosis of Lung Cancer & Lung Nodule using Machine Learning Techniques - Sangeeta Devi, Pranjal Maurya, Munish Saran, Rajan Kumar, Upendra Nath Tripathi - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.12760
DOI	https://doi.org/10.36948/ijfmr.2024.v06i01.12760
Short DOI	https://doi.org/gthqrm

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International Journal For Multidisciplinary Research

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Diagnosis and Prognosis of Lung Cancer & Lung Nodule using Machine Learning Techniques

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