Version 1
: Received: 26 June 2020 / Approved: 28 June 2020 / Online: 28 June 2020 (09:56:30 CEST)
How to cite:
Bandyopadhyay, S.; Dutta, S. Early Lung Cancer Prediction Using Neural Network with Cross-Validation. Preprints2020, 2020060333. https://doi.org/10.20944/preprints202006.0333.v1
Bandyopadhyay, S.; Dutta, S. Early Lung Cancer Prediction Using Neural Network with Cross-Validation. Preprints 2020, 2020060333. https://doi.org/10.20944/preprints202006.0333.v1
Bandyopadhyay, S.; Dutta, S. Early Lung Cancer Prediction Using Neural Network with Cross-Validation. Preprints2020, 2020060333. https://doi.org/10.20944/preprints202006.0333.v1
APA Style
Bandyopadhyay, S., & Dutta, S. (2020). Early Lung Cancer Prediction Using Neural Network with Cross-Validation. Preprints. https://doi.org/10.20944/preprints202006.0333.v1
Chicago/Turabian Style
Bandyopadhyay, S. and Shawni Dutta. 2020 "Early Lung Cancer Prediction Using Neural Network with Cross-Validation" Preprints. https://doi.org/10.20944/preprints202006.0333.v1
Abstract
Lung cancer is known as lung carcinoma. It is a disease which is malignant tumor leading to the uncontrolled cell growth in the lung tissue. Lung Cancer disease is one of the most prominent cause of death in all over world. Early detection of this disease can assist medical care unit as well as physicians to provide counter measures to the patients. The objective of this paper is to approach an automated tool that takes influential causes of lung cancer as input and detect patients with higher probabilities of being affected by this disease. A neural network classifier accompanied by cross-validation technique is proposed in this paper as a predictive tool. Later, this proposed method is compared with another baseline classifier Gradient Boosting Classifier in order to justify the prediction performance.
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
