PREDICTION LUNG CANCER BASED CRITICAL FACTORS USING MACHINE LEARNING

Authors

  • Scherko H. Murad Computer Science Department, Kurdistan Technical Institute, Sulaimani, Kurdistan Region, Iraq.
  • Ardalan H. Awlla Department of Computer Science, Cihan University Sulaimaniya, Sulaymaniya 46001, Kurdistan Region, Iraq.
  • Brzu T. Moahmmed Computer Science Department, Kurdistan Technical Institute, Sulaimani, Kurdistan Region, Iraq.

DOI:

https://doi.org/10.25271/sjuoz.2023.11.3.1105

Keywords:

Lung cancer, machine learning, SVM, RF

Abstract

Many people around the world have lung cancer. Lung cancer has a poor prognosis and a high mortality rate. Through image recognition and data analytics, computers can play a significant role in detecting various types of cancer disease. This paper provides an effective method to predict lung cancer in an early stage with high accuracy ratio. This research proposed data analytics to determine the accuracy ratio of lung cancer patients using supervised machine learning algorithms (Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The dataset for this study was obtained from "Data World," which contains 1,000 diseases. Machine learning algorithms enable us to identify lung cancer risk factors, which aid in diagnosing lung cancer. This study shows that those algorithms can classify lung cancer patients, with Random Forest having the highest accuracy of 98.507%.

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Published

2023-09-25

How to Cite

Murad, S. H., Awlla, A. H., & Moahmmed, B. T. (2023). PREDICTION LUNG CANCER BASED CRITICAL FACTORS USING MACHINE LEARNING. Science Journal of University of Zakho, 11(3), 447–452. https://doi.org/10.25271/sjuoz.2023.11.3.1105

Issue

Vol. 11 No. 3 (2023): July-September issue

Section

Science Journal of University of Zakho

License

Copyright (c) 2023 Scherko H. Murad, Ardalan H. Awlla, Brzu T. Moahmmed

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