Analysis of lung cancer predictionusing machine learning algorithms
Keywords:
KNN classifier, decision tree, Adaboost, feature scaling, correlation matrixAbstract
With the advancement of artificial intelligence, Machine learning approaches have played a crucial role in identifying, predicting, and diagnosing malignant illnesses due to their speed and accuracy. In the healthcare industry, machine learning algorithms like Logistic Regression, KNN Classifier, Support Vector Machine, Decision Tree, Nave Bayes, Random Forest, AdaBoost, Stacking Classifier, and Voting Classifier have contributed to the analysis and prediction of lung cancer prognosis [1]. This paper discusses the comparative study of all nine algorithms mentioned above. As a result, instead of referring to many publications, this document will assist scholars in swiftly scanning the project results.
Downloads
References
R. P.R., R. A. S. Nair and V. G., "A Comparative Study of Lung Cancer Detection using Machine Learning Algorithms," 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2019, pp. 1-4, doi: 10.1109/ICECCT.2019.8869001.
Waykule Jyoti, Sushmita S. Patil, Aishwarya V. Budhe, Rutuja R. Chavan. Lung Cancer Detection Using Machine Learning (ISSN-2349-5162)
Pradeep K R, Naveen N C. Lung Cancer Survivability Prediction based on Performance Using Classification Techniques of Support Vector Machines, C4.5 and Naive Bayes Algorithms for Healthcare Analytics, Procedia Computer Science, Volume 132, 2018, Pages 412-420, ISSN 1877-0509,
D. Jayaraj and S. Sathiamoorthy, "Random Forest based Classification Model for Lung Cancer Prediction on Computer Tomography Images," 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), 2019, pp. 100-104, doi: 10.1109/ICSSIT46314.2019.8987772.
Sokolova, M., Japkowicz, N., Szpakowicz, S. (2006). Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_114
Zhou, J.; Gandomi, A.H.; Chen, F.; Holzinger, A. Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics. Electronics 2021, 10, 593. https://doi.org/10.3390/ electronics10050593
N. D. Marom, L. Rokach and A. Shmilovici, "Using the confusion matrix for improving ensemble classifiers," 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel, 2010, pp. 000555-000559, doi: 10.1109/EEEI.2010.5662159.
S. S. Raoof, M. A. Jabbar and S. A. Fathima, "Lung Cancer Prediction using Machine Learning: A Comprehensive Approach," 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 2020, pp. 108-115, doi: 10.1109/ICIMIA48430.2020.9074947.
Published
How to Cite
Issue
Section
Copyright (c) 2022 International journal of health sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the International Journal of Health Sciences (IJHS) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJHS right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.
Articles published in IJHS can be copied, communicated and shared in their published form for non-commercial purposes provided full attribution is given to the author and the journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
This copyright notice applies to articles published in IJHS volumes 4 onwards. Please read about the copyright notices for previous volumes under Journal History.
