International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 1 January-February 2024 Submit your research before last 3 days of February to publish your research paper in the issue of January-February.

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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