Re-occuring-Cancer-Classifier

Use machine learning to classify whether or not the breast cancer will reccur in a particular patient.

There the algorithm used is K-nearest neighbours. Support vector machine would be about 70% accurate. SVM is particularly ineffective here because we have 201 instances of no recurrence events, and 85 instances of recurrence events. Dividing the number of recurrence events by the total dataset we get approximately 70.3% accuracy. This is the equivalent of a patient asking you whether or not they will get breast cancer again, and no matter what telling them that they will not.

The interesting thing is that 30% of the time, the algorithm will say that the cancer will reccur. The algorithm did not simply choose a single answer to stick with. Cancer is a complex issue, and the data was not one that could be reasonably seperated by what is essentially a straight line. So the optimization process essentially adjusted the vector, so that 70% of the time, when I ask whether or not the cancer will reccur, the data will fall on the non-reccurring side of the line. The K-nearest neighbours, although having its own flaws, is much more appropriate for the data at hand.

Acknowledgments

Code: This code is heavily based off of Sentdex's algorithm for classifying between benign and malignant cancer. I essentially modified that code to work with another breast-cancer dataset. His website has many useful tutorials, and I highly recommend them: https://pythonprogramming.net/

I also modified the dataset so that it would be purely comprised of numbers, no strings or ranges of numbers (i.e. 1-5) as indexes. This way the algorithm would be prepared to work with the data.

Data: Citation Request: This breast cancer domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data. Please include this citation if you plan to use this database.

1. Title: Breast cancer data (Michalski has used this)

2. Sources: -- 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) -- Date: 11 July 1988

3. Past Usage: (Several: here are some) -- 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. -- accuracy range: 66%-72% -- 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. -- 8 test results given: 65%-72% accuracy range -- 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. -- 4 systems tested: accuracy range was 68%-73.5% -- 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. -- Assistant-86: 78% accuracy

4. Relevant 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.

5. Number of Instances: 286

6. Number of Attributes: 9 + the class attribute

7. Attribute Information:

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

8. irradiat: yes=1, no=0.

9. Missing Attribute Values: (denoted by "?") Attribute #: Number of instances with missing values: 6. 8 9. 1.

10. Class Distribution:

    1. no-recurrence-events: 201 instances
    2. recurrence-events: 85 instances