journal article·2021Open access

Performance of Lung Cancer Prediction Methods Using Different Classification Algorithms

Yasemin G黮tepe

Computers, Materials & Continua· Volume 67· Issue 2

Abstract

In 2018, 1.76 million people worldwide died of lung cancer. Most of these deaths are due to late diagnosis, and early-stage diagnosis significantly increases the likelihood of a successful treatment for lung cancer. Machine l... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Artificial Intelligence in Healthcare · AI in cancer detection · Brain Tumor Detection and Classification

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (43)

Show source-provided references
1. World Health Organization (WHO), “Cancer,” 2018. [Online]. Available: https://www.who.int/en/news-room/fact-sheets/detail/cancer.
2. Brain tumor detection using statistical and machine learning method10.1016/j.cmpb.2019.05.015 ↗
3. Salem · Machine Learning Applications in Cancer Informatics · 2013 · 1
4. Preliminary study for the evaluation of the hematological blood parameters of seabream with machine learning classification methods
5. Marsland · Machine Learning: An Algorithmic Perspective · 2009
6. A survey on the application of genetic programming to classification10.1109/TSMCC.2009.2033566 ↗
7. Machine learning on machine learning models for classification of lung cancer and selection of genomic markers using array gene expression data
8. Prediction of lung cancer patient survival via supervised machine learning classification techniques10.1016/j.ijmedinf.2017.09.013 ↗
9. Misra · Machine Learning for Subsurface Characterization · 2020 · 12910.1016/B978-0-12-817736-5.00005-3 ↗
10. A practical overview of quantitative structure-activity relationship
11. Statistical normalization and back propagation for classification
12. Prediction of Bitcoin prices with machine learning methods using time series data
13. A heuristic training for support vector regression10.1016/j.neucom.2003.11.012 ↗
14. Bayes optimality in linear discriminant analysis10.1109/TPAMI.2007.70717 ↗
15. PCA versus LDA10.1109/34.908974 ↗
16. Feature selection in machine learning: A new perspective
17. A comparative assessment on air pollution estimation by machine learning algorithms10.31590/ejosat.530347 ↗
18. Aliferis · Proc AMIA Sym. · 2002 · 7
19. Lung Cancer Dataset, “UCI Machine Learning Repository,” Irvine, CA: University of California, School of Information and Computer Science, 2019.
20. Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition10.1016/S0031-3203(03)00044-X ↗
21. Machine learning in bioinformatics: A brief survey and recommendations for practitioners10.1016/j.compbiomed.2005.09.002 ↗
22. Machine learning techniques and chi-square feature selection for cancer classification using sage gene expression profiles10.1007/11691730_11 ↗
23. Feature selection based on information gain
24. Advances in Machine learning for processing and comparison of metagenomic data
25. Relief-based feature selection: Introduction and review10.1016/j.jbi.2018.07.014 ↗
26. A new accuracy measure based on bounded relative error for time series forecasting
27. Machine learning applications in cancer prognosis and prediction10.1016/j.csbj.2014.11.005 ↗
28. Effect of intuitionistic fuzzy normalization in microarray gene selection
29. Dimensionality reduction using PCA and K-Means clustering for breast cancer prediction10.24843/LKJITI.2018.v09.i03.p08 ↗
30. Automated breast cancer diagnosis based on machine learning algorithms10.1155/2019/4253641 ↗
31. Breast cancer prognosis using a machine learning approach10.3390/cancers11030328 ↗
32. Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study10.1038/s41598-019-48769-y ↗
33. C-HMOSHSSA: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods10.1016/j.cmpb.2019.06.029 ↗
34. Machine learning on mobile: An on-device inference app for skin cancer detection
35. Machine learning for assisting cervical cancer diagnosis: An ensemble approach10.1016/j.future.2019.12.033 ↗
36. Machine learning and network analyses reveal disease subtypes of pancreatic cancer and their molecular characteristics10.1038/s41598-020-58290-2 ↗
37. A new era: Artificial intelligence and machine learning in prostate cancer10.1038/s41585-019-0193-3 ↗
38. Machine learning applications in prostate cancer magnetic resonance imaging10.1186/s41747-019-0109-2 ↗
39. Gut microbiome, big data and machine learning to promote precision medicine for cancer10.1038/s41575-020-0327-3 ↗
40. Classification of lung cancer stages with machine learning over big data healthcare framework
41. Dynamic selection of normalization techniques using data complexity measures10.1016/j.eswa.2018.04.008 ↗
42. Gene expression microarray classification using PCA–BEL10.1016/j.compbiomed.2014.09.008 ↗
43. Combination of PCA with SMOTE resampling to boost the prediction rate in lung cancer dataset10.5120/13376-0987 ↗