This dataset of breast cancer patients was obtained from the 2017 November update of the SEER Program of the NCI, which provides information on population-based cancer statistics. The dataset involved female patients with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3) diagnosed in 2006-2010. Patients with unknown tumor size, examined regional LNs, regional positive LNs, and patients whose survival months were less than 1 month were excluded; thus, 4024 patients were ultimately included.

The United States Cancer Statistics (USCS) online databases in WONDER provide cancer incidence and mortality data for the United States for the years since 1999, by year, state and metropolitan areas (MSA), age group, race, ethnicity, gender, childhood cancer classifications and cancer site. Report case counts, deaths, crude and age-adjusted incidence and death rates, and 95% confidence intervals for rates. The USCS data are the official federal statistics on cancer incidence from registries having high-quality data and cancer mortality statistics for 50 states and the District of Columbia. USCS are produced by the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI), in collaboration with the North American Association of Central Cancer Registries (NAACCR). Mortality data are provided by the Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), National Vital Statistics System (NVSS).

Cancer Rates for Lake County Illinois. Explanation of field attributes: Colorectal Cancer - Cancer that develops in the colon (the longest part of the large intestine) and/or the rectum (the last several inches of the large intestine). This is a rate per 100,000. Lung Cancer – Cancer that forms in tissues of the lung, usually in the cells lining air passages. This is a rate per 100,000. Breast Cancer – Cancer that forms in tissues of the breast. This is a rate per 100,000. Prostate Cancer – Cancer that forms in tissues of the prostate. This is a rate per 100,000. Urinary System Cancer – Cancer that forms in the organs of the body that produce and discharge urine. These include the kidneys, ureters, bladder, and urethra. This is a rate per 100,000. All Cancer – All cancers including, but not limited to: colorectal cancer, lung cancer, breast cancer, prostate cancer, and cancer of the urinary system. This is a rate per 100,000.

cancerdatahp is using data.world to share Lung cancer data data

The Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) lung cancer dataset was collected in the above-mentioned specialist hospitals over a period of three months in fall 2019. It includes CT scans of patients diagnosed with lung cancer in different stages, as well as healthy subjects. IQ-OTH/NCCD slides were marked by oncologists and radiologists in these two centers. The dataset contains a total of 1190 images representing CT scan slices of 110 cases (see Figure 1). These cases are grouped into three classes: normal, benign, and malignant. of these, 40 cases are diagnosed as malignant; 15 cases diagnosed with benign; and 55 cases classified as normal cases. The CT scans were originally collected in DICOM format. The scanner used is SOMATOM from Siemens. CT protocol includes: 120 kV, slice thickness of 1 mm, with window width ranging from 350 to 1200 HU and window center from 50 to 600 were used for reading. with breath hold at full inspiration. All images were de-identified before performing analysis. Written consent was waived by the oversight review board. The study was approved by the institutional review board of participating medical centers. Each scan contains several slices. The number of these slices range from 80 to 200 slices, each of them represents an image of the human chest with different sides and angles. The 110 cases vary in gender, age, educational attainment, area of residence and living status. Some of them are employees of the Iraqi ministries of Transport and Oil, others are farmers and gainers. Most of them come from places in the middle region of Iraq, particularly, the provinces of Baghdad, Wasit, Diyala, Salahuddin, and Babylon.

Dataset Card for Lung Cancer

  Dataset Summary

The effectiveness of cancer prediction system helps the people to know their cancer risk with low cost and it also helps the people to take the appropriate decision based on their cancer risk status. The data is collected from the website online lung cancer prediction system .

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/lung-cancer.

Source:

Creators: Matjaz Zwitter & Milan Soklic (physicians)
Institute of Oncology University Medical Center
Ljubljana, Yugoslavia

Donors:
Ming Tan and Jeff Schlimmer (Jeffrey.Schlimmer '@' a.gp.cs.cmu.edu)

Data Set Information:

This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also lymphography and primary-tumor.)
This data set includes 201 instances of one class and 85 instances of another class. The instances are described by 9 attributes, some of which are linear and some are nominal.

Attribute Information:

1. Class: no-recurrence-events, recurrence-events
2. age: 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99.
3. menopause: lt40, ge40, premeno.
4. tumor-size: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59.
5. inv-nodes: 0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20, 21-23, 24-26, 27-29, 30-32, 33-35, 36-39.
6. node-caps: yes, no.
7. deg-malig: 1, 2, 3.
8. breast: left, right.
9. breast-quad: left-up, left-low, right-up, right-low, central.
10. irradiat: yes, no.

Relevant Papers:

Michalski,R.S., Mozetic,I., Hong,J., & Lavrac,N. (1986). The Multi-Purpose Incremental Learning System AQ15 and its Testing Application to Three Medical Domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, 1041-1045, Philadelphia, PA: Morgan Kaufmann.
Clark,P. & Niblett,T. (1987). Induction in Noisy Domains. In Progress in Machine Learning (from the Proceedings of the 2nd European Working Session on Learning), 11-30, Bled, Yugoslavia: Sigma Press.
Tan, M., & Eshelman, L. (1988). Using weighted networks to represent classification knowledge in noisy domains. Proceedings of the Fifth International Conference on Machine Learning, 121-134, Ann Arbor, MI.
Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users. In I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press.

Papers That Cite This Data Set1:

• Igor Fischer and Jan Poland. Amplifying the Block Matrix Structure for Spectral Clustering. Telecommunications Lab. 2005.
  • Saher Esmeir and Shaul Markovitch. Lookahead-based algorithms for anytime induction of decision trees. ICML. 2004.
  • Gavin Brown. Diversity in Neural Network Ensembles. The University of Birmingham. 2004.
  • Kaizhu Huang and Haiqin Yang and Irwin King and Michael R. Lyu and Laiwan Chan. Biased Minimax Probability Machine for Medical Diagnosis. AMAI. 2004.
  • Qingping Tao Ph. D. MAKING EFFICIENT LEARNING ALGORITHMS WITH EXPONENTIALLY MANY FEATURES. Qingping Tao A DISSERTATION Faculty of The Graduate College University of Nebraska In Partial Fulfillment of Requirements. 2004.
  • Krzysztof Grabczewski and Wl/odzisl/aw Duch. Heterogeneous Forests of Decision Trees. ICANN. 2002.
  • Hussein A. Abbass. An evolutionary artificial neural networks approach for breast cancer diagnosis. Artificial Intelligence in Medicine, 25. 2002.
  • Fei Sha and Lawrence K. Saul and Daniel D. Lee. Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines. NIPS. 2002.
  • Kristin P. Bennett and Ayhan Demiriz and Richard Maclin. Exploiting unlabeled data in ensemble methods. KDD. 2002.
  • Baback Moghaddam and Gregory Shakhnarovich. Boosted Dyadic Kernel Discriminants. NIPS. 2002.
  • András Antos and Balázs Kégl and Tamás Linder and Gábor Lugosi. Data-dependent margin-based generalization bounds for classification. Journal of Machine Learning Research, 3. 2002.
  • Michael G. Madden. Evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm. CoRR, csLG/0211003. 2002.
  • Yongmei Wang and Ian H. Witten. Modeling for Optimal Probability Prediction. ICML. 2002.
  • Remco R. Bouckaert. Accuracy bounds for ensembles under 0 { 1 loss. Xtal Mountain Information Technology & Computer Science Department, University of Waikato. 2002.
  • Nikunj C. Oza and Stuart J. Russell. Experimental comparisons of online and batch versions of bagging and boosting. KDD. 2001.
  • Bernhard Pfahringer and Geoffrey Holmes and Richard Kirkby. Optimizing the Induction of Alternating Decision Trees. PAKDD. 2001.
  • Robert Burbidge and Matthew Trotter and Bernard F. Buxton and Sean B. Holden. STAR - Sparsity through Automated Rejection. IWANN (1). 2001.
  • Bernhard Pfahringer and Geoffrey Holmes and Gabi Schmidberger. Wrapping Boosters against Noise. Australian Joint Conference on Artificial Intelligence. 2001.
  • W. Nick Street and Yoo-Hyon Kim. A streaming ensemble algorithm (SEA) for large-scale classification. KDD. 2001.
  • Lorne Mason and Peter L. Bartlett and Jonathan Baxter. Improved Generalization Through Explicit Optimization of Margins. Machine Learning, 38. 2000.
  • Endre Boros and Peter Hammer and Toshihide Ibaraki and Alexander Kogan and Eddy Mayoraz and Ilya B. Muchnik. An Implementation of Logical Analysis of Data. IEEE Trans. Knowl. Data Eng, 12. 2000.
  • P. S and Bradley K. P and Bennett A. Demiriz. Constrained K-Means Clustering. Microsoft Research Dept. of Mathematical Sciences One Microsoft Way Dept. of Decision Sciences and Eng. Sys. 2000.
  • Sally A. Goldman and Yan Zhou. Enhancing Supervised Learning with Unlabeled Data. ICML. 2000.
  • Justin Bradley and Kristin P. Bennett and Bennett A. Demiriz. Constrained K-Means Clustering. Microsoft Research Dept. of Mathematical Sciences One Microsoft Way Dept. of Decision Sciences and Eng. Sys. 2000.
  • Yuh-Jeng Lee. Smooth Support Vector Machines. Preliminary Thesis Proposal Computer Sciences Department University of Wisconsin. 2000.
  • Petri Kontkanen and Petri Myllym and Tomi Silander and Henry Tirri and Peter Gr. On predictive distributions and Bayesian networks. Department of Computer Science, Stanford University. 2000.
  • Kristin P. Bennett and Ayhan Demiriz and John Shawe-Taylor. A Column Generation Algorithm For Boosting. ICML. 2000.
  • Matthew Mullin and Rahul Sukthankar. Complete Cross-Validation for Nearest Neighbor Classifiers. ICML. 2000.
  • David W. Opitz and Richard Maclin. Popular Ensemble Methods: An Empirical Study. J. Artif. Intell. Res. (JAIR, 11. 1999.
  • Chun-Nan Hsu and Hilmar Schuschel and Ya-Ting Yang. The ANNIGMA-Wrapper Approach to Neural Nets Feature Selection for Knowledge Discovery and Data Mining. Institute of Information Science. 1999.
  • David M J Tax and Robert P W Duin. Support vector domain description. Pattern Recognition Letters, 20. 1999.
  • Kai Ming Ting and Ian H. Witten. Issues in Stacked Generalization. J. Artif. Intell. Res. (JAIR, 10. 1999.
  • Ismail Taha and Joydeep Ghosh. Symbolic Interpretation of Artificial Neural Networks. IEEE Trans. Knowl. Data Eng, 11. 1999.
  • Lorne Mason and Jonathan Baxter and Peter L. Bartlett and Marcus Frean. Boosting Algorithms as Gradient Descent. NIPS. 1999.
  • Iñaki Inza and Pedro Larrañaga and Basilio Sierra and Ramon Etxeberria and Jose Antonio Lozano and Jos Manuel Peña. Representing the behaviour of supervised classification learning algorithms by Bayesian networks. Pattern Recognition Letters, 20. 1999.
  • Lorne Mason and Peter L. Bartlett and Jonathan Baxter. Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS. 1998.
  • Richard Maclin. Boosting Classifiers Regionally. AAAI/IAAI. 1998.
  • Huan Liu and Hiroshi Motoda and Manoranjan Dash. A Monotonic Measure for Optimal Feature Selection. ECML. 1998.
  • Yk Huhtala and Juha Kärkkäinen and Pasi Porkka and Hannu Toivonen. Efficient Discovery of Functional and Approximate Dependencies Using Partitions. ICDE. 1998.
  • W. Nick Street. A Neural Network Model for Prognostic Prediction. ICML. 1998.
  • Kristin P. Bennett and Erin J. Bredensteiner. A Parametric Optimization Method for Machine Learning. INFORMS Journal on Computing, 9. 1997.
  • Pedro Domingos. Control-Sensitive Feature Selection for Lazy Learners. Artif. Intell. Rev, 11. 1997.
  • Rudy Setiono and Huan Liu. NeuroLinear: From neural networks to oblique decision rules. Neurocomputing, 17. 1997.
  • . Prototype Selection for Composite Nearest Neighbor Classifiers. Department of Computer Science University of Massachusetts. 1997.
  • Erin J. Bredensteiner and Kristin P. Bennett. Feature Minimization within Decision Trees. National Science Foundation. 1996.
  • Ismail Taha and Joydeep Ghosh. Characterization of the Wisconsin Breast cancer Database Using a Hybrid Symbolic-Connectionist System. Proceedings of ANNIE. 1996.
  • Kamal Ali and Michael J. Pazzani. Error Reduction through Learning Multiple Descriptions. Machine Learning, 24. 1996.
  • Jennifer A. Blue and Kristin P. Bennett. Hybrid Extreme Point Tabu Search. Department of Mathematical Sciences Rensselaer Polytechnic Institute. 1996.
  • Pedro Domingos. Unifying Instance-Based and Rule-Based Induction. Machine Learning, 24. 1996.
  • Geoffrey I. Webb. OPUS: An Efficient Admissible Algorithm for Unordered Search. J. Artif. Intell. Res. (JAIR, 3. 1995.
  • Christophe Giraud and Tony Martinez and Christophe G. Giraud-Carrier. University of Bristol Department of Computer Science ILA: Combining Inductive Learning with Prior Knowledge and Reasoning. 1995.
  • Ron Kohavi. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. IJCAI. 1995.
  • M. A. Galway and Michael G. Madden. DEPARTMENT OF INFORMATION TECHNOLOGY technical report NUIG-IT-011002 Evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm. Department of Information Technology National University of Ireland, Galway.
  • John G. Cleary and Leonard E. Trigg. Experiences with OB1, An Optimal Bayes Decision Tree Learner. Department of Computer Science University of Waikato.
  • Wl/odzisl/aw Duch and Rafal/ Adamczak Email:duchraad@phys. uni. torun. pl. Statistical methods for construction of neural networks. Department of Computer Methods, Nicholas Copernicus University.
  • Rong-En Fan and P. -H Chen

The dataset is maintained by VISION AND IMAGE PROCESSING LAB, University of Waterloo. The images of the dataset were extracted from the public databases DermIS and DermQuest, along with manual segmentations of the lesions.

The dataset was used in the following journal publication. [1] Glaister, J., A. Wong, and D. A. Clausi, "Automatic segmentation of skin lesions from dermatological photographs using a joint probabilistic texture distinctiveness approach", IEEE Transactions on Biomedical Engineering [2] Amelard, R., J. Glaister, A. Wong, and D. A. Clausi, "High-level intuitive features (HLIFs) for intuitive skin lesion descriptionpdf", IEEE Transactions on Biomedical Engineering, vol. 62, issue 3, pp. 820-831, October, 2015. [3] Glaister, J., R. Amelard, A. Wong, and D. A. Clausi, "MSIM: Multi-Stage Illumination Modeling of Dermatological Photographs for Illumination-Corrected Skin Lesion Analysis", IEEE Transactions on Biomedical Engineering, vol. 60, issue 7, pp. 1873 - 1883, November, 2013.

Curated Breast Imaging Subset DDSM Dataset (Mammography)

Source:

Creators:
1. Dr. William H. Wolberg, General Surgery Dept. University of Wisconsin, Clinical Sciences Center Madison, WI 53792wolberg '@' eagle.surgery.wisc.edu
2. W. Nick Street, Computer Sciences Dept. University of Wisconsin, 1210 West Dayton St., Madison, WI 53706street '@' cs.wisc.edu 608-262-6619
3. Olvi L. Mangasarian, Computer Sciences Dept. University of Wisconsin, 1210 West Dayton St., Madison, WI 53706olvi '@' cs.wisc.edu

Donor:
Nick Street

Data Set Information:

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at
Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.
The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].
This database is also available through the UW CS ftp server:ftp ftp.cs.wisc.educd math-prog/cpo-dataset/machine-learn/WDBC/

Attribute Information:

1) ID number
2) Diagnosis (M = malignant, B = benign)
3-32)

Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" - 1)

Relevant Papers:

First Usage:
W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology, volume 1905, pages 861-870, San Jose, CA, 1993.

O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pages 570-577, July-August 1995. Medical literature:

W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) 163-171.

W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Analytical and Quantitative Cytology and Histology, Vol. 17 No. 2, pages 77-87, April 1995.

W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. Computerized breast cancer diagnosis and prognosis from fine needle aspirates. Archives of Surgery 1995;130:511-516.

W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. Computer-derived nuclear features distinguish malignant from benign breast cytology. Human Pathology, 26:792*796, 1995. See also:

Papers That Cite This Data Set1:

• Gavin Brown. Diversity in Neural Network Ensembles. The University of Birmingham. 2004.
  • Hussein A. Abbass. An evolutionary artificial neural networks approach for breast cancer diagnosis. Artificial Intelligence in Medicine, 25. 2002.
  • Baback Moghaddam and Gregory Shakhnarovich. Boosted Dyadic Kernel Discriminants. NIPS. 2002.
  • Krzysztof Grabczewski and Wl/odzisl/aw Duch. Heterogeneous Forests of Decision Trees. ICANN. 2002.
  • András Antos and Balázs Kégl and Tamás Linder and Gábor Lugosi. Data-dependent margin-based generalization bounds for classification. Journal of Machine Learning Research, 3. 2002.
  • Kristin P. Bennett and Ayhan Demiriz and Richard Maclin. Exploiting unlabeled data in ensemble methods. KDD. 2002.
  • Robert Burbidge and Matthew Trotter and Bernard F. Buxton and Sean B. Holden. STAR - Sparsity through Automated Rejection. IWANN (1). 2001.
  • Nikunj C. Oza and Stuart J. Russell. Experimental comparisons of online and batch versions of bagging and boosting. KDD. 2001.
  • Yuh-Jeng Lee. Smooth Support Vector Machines. Preliminary Thesis Proposal Computer Sciences Department University of Wisconsin. 2000.
  • Justin Bradley and Kristin P. Bennett and Bennett A. Demiriz. Constrained K-Means Clustering. Microsoft Research Dept. of Mathematical Sciences One Microsoft Way Dept. of Decision Sciences and Eng. Sys. 2000.
  • Lorne Mason and Peter L. Bartlett and Jonathan Baxter. Improved Generalization Through Explicit Optimization of Margins. Machine Learning, 38. 2000.
  • P. S and Bradley K. P and Bennett A. Demiriz. Constrained K-Means Clustering. Microsoft Research Dept. of Mathematical Sciences One Microsoft Way Dept. of Decision Sciences and Eng. Sys. 2000.
  • Endre Boros and Peter Hammer and Toshihide Ibaraki and Alexander Kogan and Eddy Mayoraz and Ilya B. Muchnik. An Implementation of Logical Analysis of Data. IEEE Trans. Knowl. Data Eng, 12. 2000.
  • Chun-Nan Hsu and Hilmar Schuschel and Ya-Ting Yang. The ANNIGMA-Wrapper Approach to Neural Nets Feature Selection for Knowledge Discovery and Data Mining. Institute of Information Science. 1999.
  • Lorne Mason and Peter L. Bartlett and Jonathan Baxter. Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS. 1998.
  • W. Nick Street. A Neural Network Model for Prognostic Prediction. ICML. 1998.
  • Yk Huhtala and Juha Kärkkäinen and Pasi Porkka and Hannu Toivonen. Efficient Discovery of Functional and Approximate Dependencies Using Partitions. ICDE. 1998.
  • Huan Liu and Hiroshi Motoda and Manoranjan Dash. A Monotonic Measure for Optimal Feature Selection. ECML. 1998.
  • Kristin P. Bennett and Erin J. Bredensteiner. A Parametric Optimization Method for Machine Learning. INFORMS Journal on Computing, 9. 1997.
  • Rudy Setiono and Huan Liu. NeuroLinear: From neural networks to oblique decision rules. Neurocomputing, 17. 1997.
  • . Prototype Selection for Composite Nearest Neighbor Classifiers. Department of Computer Science University of Massachusetts. 1997.
  • Jennifer A. Blue and Kristin P. Bennett. Hybrid Extreme Point Tabu Search. Department of Mathematical Sciences Rensselaer Polytechnic Institute. 1996.
  • Erin J. Bredensteiner and Kristin P. Bennett. Feature Minimization within Decision Trees. National Science Foundation. 1996.
  • Ismail Taha and Joydeep Ghosh. Characterization of the Wisconsin Breast cancer Database Using a Hybrid Symbolic-Connectionist System. Proceedings of ANNIE. 1996.
  • Geoffrey I. Webb. OPUS: An Efficient Admissible Algorithm for Unordered Search. J. Artif. Intell. Res. (JAIR, 3. 1995.
  • Rudy Setiono. Extracting M-of-N Rules from Trained Neural Networks. School of Computing National University of Singapore.
  • Jarkko Salojarvi and Samuel Kaski and Janne Sinkkonen. Discriminative clustering in Fisher metrics. Neural Networks Research Centre Helsinki University of Technology.
  • Wl odzisl and Rafal Adamczak and Krzysztof Grabczewski and Grzegorz Zal. A hybrid method for extraction of logical rules from data. Department of Computer Methods, Nicholas Copernicus University.
  • Charles Campbell and Nello Cristianini. Simple Learning Algorithms for Training Support Vector Machines. Dept. of Engineering Mathematics.
  • Chotirat Ann and Dimitrios Gunopulos. Scaling up the Naive Bayesian Classifier: Using Decision Trees for Feature Selection. Computer Science Department University of California.
  • Wl odzisl/aw Duch and Rudy Setiono and Jacek M. Zurada. Computational intelligence methods for rule-based data understanding.
  • Rafael S. Parpinelli and Heitor S. Lopes and Alex Alves Freitas. An Ant Colony Based System for Data Mining: Applications to Medical Data. CEFET-PR, CPGEI Av. Sete de Setembro, 3165.
  • Wl/odzisl/aw Duch and Rafal/ Adamczak Email:duchraad@phys. uni. torun. pl. Statistical methods for construction of neural networks. Department of Computer Methods, Nicholas Copernicus University.
  • Rafael S. Parpinelli and Heitor S. Lopes and Alex Alves Freitas. PART FOUR: ANT COLONY OPTIMIZATION AND IMMUNE SYSTEMS Chapter X An Ant Colony Algorithm for Classification Rule Discovery. CEFET-PR, Curitiba.
  • Adam H. Cannon and Lenore J. Cowen and Carey E. Priebe. Approximate Distance Classification. Department of Mathematical Sciences The Johns Hopkins University.
  • Andrew I. Schein and Lyle H. Ungar. A-Optimality for Active Learning of Logistic Regression Classifiers. Department of Computer and Information Science Levine Hall.
  • Bart Baesens and Stijn Viaene and Tony Van Gestel and J. A. K Suykens and Guido Dedene and Bart De Moor and Jan Vanthienen and Katholieke Universiteit Leuven. An Empirical Assessment of Kernel Type Performance for Least Squares Support Vector Machine Classifiers. Dept. Applied Economic Sciences.
  • Adil M. Bagirov and Alex Rubinov and A. N. Soukhojak and John Yearwood. Unsupervised and supervised data classification via nonsmooth and global optimization. School of Information Technology and Mathematical Sciences, The University of Ballarat.
  • Rudy Setiono and Huan Liu. Neural-Network Feature Selector. Department of Information Systems and Computer Science National University of Singapore.
  • Huan Liu. A Family of Efficient Rule Generators. Department of Information Systems and Computer Science National University of Singapore.

Citation Request:

Please refer to the Machine Learning Repository's citation policy. [1] Papers were automatically harvested and associated with this data set, in collaborationwith

Users can access data about cancer statistics in the United States including but not limited to searches by type of cancer and race, sex, ethnicity, age at diagnosis, and age at death. Background Surveillance Epidemiology and End Results (SEER) database’s mission is to provide information on cancer statistics to help reduce the burden of disease in the U.S. population. The SEER database is a project to the National Cancer Institute. The SEER database collects information on incidence, prevalence, and survival from specific geographic areas representing 28 percent of the United States population. User functionality Users can access a variety of reso urces. Cancer Stat Fact Sheets allow users to look at summaries of statistics by major cancer type. Cancer Statistic Reviews are available from 1975-2008 in table format. Users are also able to build their own tables and graphs using Fast Stats. The Cancer Query system provides more flexibility and a larger set of cancer statistics than F ast Stats but requires more input from the user. State Cancer Profiles include dynamic maps and graphs enabling the investigation of cancer trends at the county, state, and national levels. SEER research data files and SEER*Stat software are available to download through your Internet connection (SEER*Stat’s client-server mode) or via discs shipped directly to you. A signed data agreement form is required to access the SEER data Data Notes Data is available in different formats depending on which type of data is accessed. Some data is available in table, PDF, and html formats. Detailed information about the data is available under “Data Documentation and Variable Recodes”.

Data comes from two different sources. Population-based lung cancer incidence rates for the period 2010-2014 (most updated data) were abstracted from National Cancer Institute state cancer profiles (Schwartz et al. 1996).This national county-level database of cancer data is collected by state public health surveillance systems. The domain specific county level environmental quality index (EQI) data for the period 2000-2005 were abstracted from United States Environmental Protection Agency (USEPA) profile. Complete descriptions of the datasets used in the EQI are provided in Lobdell’s paper (Lobdell 2011). Data were merged based on the Federal Information Processing Standards (FIPS) code. Out of 3144 counties in United States this study has available information for 2602 counties: Data was not available for four states namely Kansas, Michigan, Minnesota and Nevada due to state legislation and regulations which prohibit the release of county-level data to outside entities, county whose lung cancer mortality information is missing were omitted from the data set, the Union county, Florida is an outlier in terms of mortality information which was deleted from the data set, in the process of local control analysis this study experiences two (cluster 28 and 29) non-informative clusters (non-informative cluster is one for which either treatment or control group information is missing). For analysis, non-informative clusters information was deleted from the data set. Three types of variables are used in this study: (i) lung cancer mortality as an outcome variable (ii) binary treatment indicator is the PM2.5 high (greater than 10.59 mg/m3) vs. low (less than 10.59 mg/m3) (iii) three potential X confounder for clustering namely land EQI, sociodemographic EQI and built EQI. For each index, higher values correspond to poorer environmental quality (Jagai et al. 2017). As PM2.5 is one of the indicators for measuring air EQI, that is why we do not consider the air EQI to avoid confounding effects.

Signatures of Oncogenic Pathway Deregulation in Human Cancers. The ability to define cancer subtypes, recurrence of disease, and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Such data is also of substantial importance to the analysis of cellular signaling pathways central to the oncogenic process. With this focus, we have developed a series of gene expression signatures that reliably reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumors, and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumor sub-types. Clustering tumors based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Furthermore, predictions of pathway deregulation in cancer cell lines are shown to coincide with sensitivity to therapeutic agents that target components of the pathway, underscoring the potential for such pathway prediction to guide the use of targeted therapeutics. Keywords: other Overall design: RNA was extracted from frozen tissue of primary breast tumors for gene array analysis.

Cancer diagnoses and age-standardised incidence rates for all types of cancer by age and sex including breast, prostate, lung and colorectal cancer.

The dataset contains information of 213 cancer patients undergoing clinical or surgical treatment characterized on sociodemographic and clinical data as well as data from the Care Transition Measure (CTM 15-Brazil). Data collection was carried out 7 to 30 days after their discharge from hospital from June to August 2019. Understanding these data can contribute to improving quality of care transitions and avoiding hospital readmissions. To this end, this dataset contains a broad array of variables:

*gender

*age group

*place of residence

*race

*marital status

*schooling

*paid work activity

*type of treatment

*cancer staging

*metastasis

*comorbidities

*main complaint

*continue use medication

*diagnosis

*cancer type

*diagnostic year

*oncology treatment

*first hospitalization

*readmission in the last 30 days

*number of hospitalizations in the last 30 days

*readmission in the last 6 months

*number of hospitalizations in the last 6 months

*readmission in the last year

*number of hospitalizations in the last year

*questions 1-15 from CTM 15-Brazil

The data are presented as a single Excel XLSX file: cancer patient´s care transitions dataset.xlsx.

The analyses of the present dataset have the potential to generate hospital readmission prevention strategies to be implemented by the hospital team. Researchers who are interested in CTs of cancer patients can extensively explore the variables described here.

The project from which these data were extracted was approved by the institution’s research ethics committee (approval n. 3.266.259/2019) at Associação Hospital de Caridade Ijuí, Rio Grande do Sul, Brazil.

The Breast Cancer Histopathological Image Classification (BreakHis) is composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients using different magnifying factors (40X, 100X, 200X, and 400X). It contains 2,480 benign and 5,429 malignant samples (700X460 pixels, 3-channel RGB, 8-bit depth in each channel, PNG format). This database has been built in collaboration with the P&D Laboratory - Pathological Anatomy and Cytopathology, Parana, Brazil.

Paper: F. A. Spanhol, L. S. Oliveira, C. Petitjean and L. Heutte, "A Dataset for Breast Cancer Histopathological Image Classification," in IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1455-1462, July 2016, doi: 10.1109/TBME.2015.2496264

Oncologist Jamilya Polatova spoke about cancer treatmentAt the same time, the percentage of cancer incidence continues to grow. What is the reason? But you are right thatscience does not stand still - the possibilities of diagnostics have gone far ahead.Cancer screening increases the proportion of oncological pathology detected at an early stage.stage of the disease, while increasing the chances of recovery. That is, thanThe earlier cancer is detected, the higher the chance of recovery. To the deepUnfortunately, not all patients come to us at the early stages, and therefore,It is customary to talk about mortality from oncology, but what is more -deaths or recoveries? BUTwhat is difficult to access and is detected in the later stages, unfortunately, increasesmortality.What types of diagnostics are most effective? From what ageDo you recommend regular checkups?It is statistically proven that 60% of new cases occur in the ten mostcommon types of cancer. In women, this is, for example, breast cancerand cervix. However, regular monitoring and examinationwith a specialist allows you to detect oncology at an early stage. Optionsdiagnostics are different, as a rule it is mammography (iffor a breast tumor), EGDFS, colonoscopy and others. At the same time, the mainHistology has been and remains the diagnostic method. Unfortunately, it must be admitted thatcancer tends to rejuvenate.Is it possible to detect oncology by a general blood test?If there are any changes in the analyzes, then most likely it is already strongadvanced stage, but this is when we are talking specifically about oncology. At the earlystages, unfortunately, it is impossible to find changes in the analyzes, for which weand carry out diagnostics. Unfortunately, cancer is difficult to detect, as earlystages, it practically does not give any symptoms. Even in case of externallocalizations, such as skin, mammary gland, oral cavity, often patientsasking for help late.You have been in the profession for 20 years, have you ever had the desire to quit everything?I can't quit, what are you? This is what I live by. In addition, in medicine it is importantthe principle of continuity of generations, you need to transfer all your knowledge and experienceto the next generation, this is the school. Moreover, we not only educateprofessionals, they should be just as desperate and in love with the professionpeople with the ability to empathize and do everything possible to savelives. Luckily, those are the ones next to me. We are currently developing the followinggeneration of oncologists, talented, progressive-minded and creative, with thisAs a goal, we organized the Onco-School.The idea of ​​creating the Onco School came to me during the lockdown, when everyonefaced a severe shortage

The Hallmarks of Cancer (HOC) Corpus consists of 1852 PubMed publication abstracts manually annotated by experts according to a taxonomy. The taxonomy consists of 37 classes in a hierarchy. Zero or more class labels are assigned to each sentence in the corpus. The labels are found under the "labels" directory, while the tokenized text can be found under "text" directory. The filenames are the corresponding PubMed IDs (PMID).

Breast MRI scans of 922 cancer patients from Duke University, with tumor bounding box annotations, clinical, imaging, and many other features, and more.