No Detail Found!

Lung cancer detection using supervised machine learning techniques

Author(s)
Rahat Idrees Department of Computer Science, NFC Institute of Engineering and Technology, Multan, Pakistan
Muhammad Kamran Abid Department of Computer Science, NFC Institute of Engineering and Technology, Multan, Pakistan
Saleem Raza Department of Electronic Engineering, Quaid-e-Awam University of Engineering, Science and Technology,Nawabshah,Pakistan
Muhammad Kashif Quaid-e-Awam University of Engineering, Science and Technology,Nawabshah,Pakistan
Muhammad Waqas Department of Computer Science and Technology, Xi'an University of Science & Technology, China
Mubashir Ali Department of Computer Science, Bahauddin Zakariya University,Multan,Pakistan
Laiba Rehman Department of Computer Science, Pakistan Institute of Engineering and Technology,Multan,Pakistan
Abstract
In recent times, Lung cancer has been the most common cause of mortality in both men and women worldwide. Lung cancer is the second most well-known disease after heart disease. Although lung cancer prevention is impossible, early detection of lung cancer can effectively treat lung cancer at an early stage. A patient's survival rate may increase if lung cancer is identified early. Various data analysis and machine learning techniques have been applied to detect and diagnose lung cancer in its early stages. In this paper, we used supervised machine learning algorithms like SVM (Support vector machine), ANN (Artificial neural networks), MLR (Multiple linear regression), and RF (random forest) to detect the early stages of lung tumours. The primary purpose of this study is to examine the success of machine learning algorithms in detecting lung cancer at an early stage. Compared to all other supervised machine learning algorithms, the Random forest model produces a high result, with a 99.99% accuracy rate.
Publication Details
Page(s) 30-36
DOI 10.54692/lgurjcsit.2022.0601276
Published Journal: Lahore Garrison University Research Journal of Computer Science and Information Technology, Volume: 6, Issue: 1, Year: 2022

Request the full text from PASTIC

Keywords
Random Forest, Machine learning, Healthcare, Lung Cancer Detection
More from Lahore Garrison University Research Journal of Computer Science and Information Technology
0

Citations

0

Downloads

19

Views

Copyright ©  PASTIC National Center, Islamabad www.pastic.gov.pk