CFP last date
22 July 2024
Call for Paper
August Edition
IJCA solicits high quality original research papers for the upcoming August edition of the journal. The last date of research paper submission is 22 July 2024

Submit your paper
Know more
Reseach Article

Prediction of Malignant and Benign Tumor using Machine Learning

by Ashish Shah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 135 - Number 5
Year of Publication: 2016
Authors: Ashish Shah

Ashish Shah . Prediction of Malignant and Benign Tumor using Machine Learning. International Journal of Computer Applications. 135, 5 ( February 2016), 19-23. DOI=10.5120/ijca2016908385

@article{ 10.5120/ijca2016908385,
author = { Ashish Shah },
title = { Prediction of Malignant and Benign Tumor using Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 135 },
number = { 5 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016908385 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:34:56.392461+05:30
%A Ashish Shah
%T Prediction of Malignant and Benign Tumor using Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 135
%N 5
%P 19-23
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Machine Learning is a branch of Computer Science that is concerned with designing systems that can learn from the provided input. Supervised Machine Learning is where the system needs to be first trained using already classified training data as opposed to an unsupervised system where no such training is required. Supervised learning comprises of 2 training techniques. Linear Regression predicts a continuous valued output. Logistic Regression, more commonly known as Classification predicts a discrete valued output. It is the algorithm for identifying to which of a set of categories a new observation belongs. In this paper we aim to assess whether a lump in a breast could be malignant (cancerous) or benign (non-cancerous) by Classification. The 2 features under consideration are Clump Thickness and Marginal Adhesion. Clump Thickness helps us detect cancerous cells as they are often grouped in multilayers whereas benign cells tend to be grouped in monolayers. Normal cells tend to stick together but Cancerous cells tend to lose this ability. So loss of Marginal Adhesion is a sign of malignancy. With the help of the sigmoid function, we find the Cost function of our data and minimize the sum of the squared errors over the training set. Using Gradient Descent we find the global minimum of our Cost function and then calculate the parameters that fit our data. Finally we estimate the probability of the patient’s tumor being malignant or benign based on the values of these 2 features and the parameters.

  1. Text Categorization Through Probabilistic Learning:Applications to Recommender Systems, Paul N. Bennett, Department of Computer Sciences, University of Texas at Austin, May 1998.
  2. Reviews of Machine Learning by Ryszard S. Michalski, Jaime Carbonell and Tom Mitchell, Tiago Publishing Company
  3. Dr. BD Prasad, PE Krishna Prasad and Y Sagar, “A Comparative Study of Machine Learning Algorithms as Expert Systems in Medical Diagnosis” – CCSIT 2011, CCIS 131
  4. Anderson, JR & Matessa, M (1992) Explorations of an incremental Bayesian algorithm forcategorization,Machine Learning
  5. Amar Gondaliya, Logistic Regression with R: Step Implmentation 2013.
  6. Pattern Analysis and Machine Intelligence, Stephen Della Pietra, Vincent Della Pietra and JohnLafferty
  7. A Theory of Learning Classification Rules,Dissertation, Dept of Computer Science, University ofTechnology, Sydney.
  8. Duda Ro & Hard PE, Pattern Classification and Scene Analysis, New York
  9. Proc. Of Innovative Application of Machine Learning, Ellen Spertus (1997)
  10. Implementation Of Clustering Through Machine Learning Tool, Sree Ram Nimmagadda, Phaneendra Kanakamedala And Vijay Bashkarreddy Yaramala
Index Terms

Computer Science
Information Sciences


Keywords are your own designated keywords which can be used for easy location of the manuscript using any search engines.