Lung Cancer

Donated on 4/30/1992

Lung cancer data; no attribute definitions

Dataset Characteristics

Multivariate

Subject Area

Health and Medicine

Associated Tasks

Classification

Feature Type

Integer

# Instances

32

# Features

56

Dataset Information

Additional Information

This data was used by Hong and Young to illustrate the power of the optimal discriminant plane even in ill-posed settings. Applying the KNN method in the resulting plane gave 77% accuracy. However, these results are strongly biased (See Aeberhard's second ref. above, or email to stefan@coral.cs.jcu.edu.au). Results obtained by Aeberhard et al. are : RDA : 62.5%, KNN 53.1%, Opt. Disc. Plane 59.4% The data described 3 types of pathological lung cancers. The Authors give no information on the individual variables nor on where the data was originally used. Notes: - In the original data 4 values for the fifth attribute were -1. These values have been changed to ? (unknown). (*) - In the original data 1 value for the 39 attribute was 4. This value has been changed to ? (unknown). (*)

Has Missing Values?

Yes

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
classTargetCategoricalno
Attribute1FeatureCategoricalno
Attribute2FeatureCategoricalno
Attribute3FeatureCategoricalno
Attribute4FeatureCategoricalyes
Attribute5FeatureCategoricalno
Attribute6FeatureCategoricalno
Attribute7FeatureCategoricalno
Attribute8FeatureCategoricalno
Attribute9FeatureCategoricalno

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Additional Variable Information

Attribute 1 is the class label. All predictive attributes are nominal, taking on integer values 0-3

Baseline Model Performance

Dataset Files

FileSize
lung-cancer.data3.6 KB
lung-cancer.names2.1 KB
Index126 Bytes

Papers Citing this Dataset

Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

By Zhiguo Zhou, Zhi-Jie Zhou, Hongxia Hao, Shulong Li, Xi Chen, You Zhang, Michael Folkert, Jing Wang. 2017

Published in ArXiv.

Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of Lung Cancer Disease

By Mitra Montazeri, Mahdieh Baghshah, Ahmad Enhesari. 2015

Published in J. Basic Appl. Sci. Res, 2013. 3(10): p. 134-140.

Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer Patients

By Kwetishe Danjuma. 2015

Published in IJCSI International Journal of Computer Science Issues, Volume 12, Issue 2, March 2015.

Dimensionality Reduction: An Empirical Study on the Usability of IFE-CF (Independent Feature Elimination- by C-Correlation and F-Correlation) Measures

By M. Reddy, L. Reddy. 2010

Published in International Journal of Computer Science Issues, IJCSI, Vol. 7, Issue 1, No. 1, January 2010, http://ijcsi.org/articles/Dimensionality-Reduction-An-Empirical-Study-on-the-Usability-of-IFE-CF-(Independent-Feature-Elimination-by-C-Correlation-and-F-Correlation)-Measures.php.

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4 citations
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Creators

Z.Q. Hong

J.Y Yang

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