Breast Cancer Detecation System project Report

1. PROJECT PHASE 2 – UIS715P
2020 – 2021
BREAST CANCER DETECTION USING
MACHINE LEARNING
B.V.V.Sangha’s
Basaveshwar Engineering College(A),
Bagalkot - 587102
Department of Information Science
P r o j e c t G u i d e : P r o f S . N . K U G A L I Submitted By :1)Shashank.S.S
2)Shreeshail.C.T
3)Vijayalaxmi.M.B
4)Allamprabhu.R.Y

2.  Wo m e n a r e s e r i o u s l y t h r e a t e n e d b y b r e a s t c a n c e r w i t h h i g h
m o r b i d i t y a n d m o r t a l i t y.
 T h e l a c k o f r o b u s t p r o g n o s i s m o d e l s r e s u l t s i n d i f f i c u l t y f o r
d o c t o r s t o p r e p a r e a t r e a t m e n t p l a n t h a t m a y p r o l o n g p a t i e n t
s u r v i v a l t i m e .
 H e n c e , t h e r e q u i r e m e n t o f t i m e i s t o d e v e l o p t h e t e c h n i q u e w h i c h
g i v e s m i n i m u m e r r o r t o i n c r e a s e a c c u r a c y. F o u r a l g o r i t h m S V M ,
L o g i s t i c R e g r e s s i o n , R a n d o m F o r e s t a n d K N N w h i c h p r e d i c t t h e
B r e a s t c a n c e r o u t c o m e h a v e b e e n c o m p a r e d i n t h e p a p e r u s i n g
d i f f e r e n t d a t a s e t s .
 A l l e x p e r i m e n t s a r e e x e c u t e d w i t h i n a s i m u l a t i o n e n v i r o n m e n t a n d
c o n d u c t e d i n J u p y t e r p l a t f o r m .
Introduction

3. Aim of Project categorizes in three domains.
First domain is prediction of cancer before diagnosis;
Second domain is prediction of diagnosis and treatment;
Third domain focuses on outcome during treatment.
The proposed work can be used to predict the outcome of different technique and suitable technique can
be used depending upon requirement. This research is carried out to predict the accuracy.
The future Project can be carried out to predict the other different parameters and breast cancer Project
can be categorizes on basis of other parameters.
O B J E C T I V S
• T h i s a n a l y s i s a i m s t o o b s e r v e w h i c h f e a t u r e s a r e m o s t h e l p f u l i n
p r e d i c t i n g m a l i g n a n t o r b e n i g n c a n c e r .
• O u r a p p l i c a t i o n w i l l r e s u l t s t h e m o r e a c c u r a t e t h a n t h e d o c t o r ’ s
p r e d i c t i o n .
• C o s t a n d t i m e e f f i c i e n t .

4. 1. Support Vector machine algorithm
2. Random forest algorithm
3. K-nearest neighbors algorithm
4. Logistic regression algorithm
ALGORITHMS USED IN THIS APPLICATION

5. Front End Design

6. DATA FILE

8. CODING PART

9. print("loading done")
wait for loading page.
from ipywidgets import widgets, Layout
here we are importing widgets from jupyter layout.
A1=widgets.Label('Radius mean (Range 0-50)')
Here we are label to that widget to radius mean.
display(A1)
displaying that label.
V1=widgets.Text()
importing text box widget.
display(V1)
displaying tat text box.
Explanation

11. Import os.path
This module contains some useful functions on pathnames, because we are importing
this.
import pandas as pd
pandas is a software library written for the Python programming language for data
manipulation and analysis.
from os import path
Here we are importing os path, for manipulate os path.
Importing matplotlib and pyplot
Pyplot is a collection of functions in the popular visualization package Matplotlib.
Its functions manipulate elements of a figure, such as creating a figure, creating a
plotting area, plotting lines, adding plot labels, etc. Let's use the plot() function from
pyplot to create a dashed line graph showing the growth of a company's stock.
Remember, you can change the color of the line by adding the argument color and the
linestlye by adding the argument linestyle.
Explanation

13. import numpy as np
numpy is an buildin function,its helps us to create and array.
import joblib as jb
Joblib is such an pacakage that can simply turn our Python code into parallel
computing mode and of course increase the computing speed.
Import json
JSON is a syntax for storing and exchanging data.
import ipywidgets as widgets
ipywidgets, also known as jupyter-widgets or simply widgets, are interactive HTML
widgets for Jupyter notebooks and the IPython kernel. Notebooks come alive when
interactive widgets are used. Users gain control of their data and can visualize
changes in the data.
Explanation

14. OUTPUT

16. •Rashmi G D, A Lekha and Neelam Bawani, “Analysis of efficiency of classification and prediction
algorithms (Naïve Bayes) for breast cancer”, International Conference on Emerging Research in
Electronics, Computer Science and Technology, 17th to 19th December, 2015, Mandya, India.
•Bo Fu, Pei Liu, Jie Lin, Ling Deng, Kejia Hu and Hong Zheng, “Predicting invasive disease–free
survival for early–stage breast cancer patients using follow up clinical data”, IEEE Transactions on
Biomedical Engineering, 2018.
•S N Singh and Shivani Thakral, “Using data mining tools for breast cancer prediction and analysis”,
4th International Conference on Computing, Communication and Automation, 14thto 15th
December, 2018, Greater, Noida, India.
•Kemal Polat and Umit Senturk, “A novel ML approach to prediction of breast cancer: combining of
mad normalization, KMC based feature weighting and adaboost classifier”, 2nd International
Symposium on Multidisciplinary Studies and Innovative Technologies, 19th to 21st October, 2018,
Ankara, Turkey.
REFERENCES

17. CONCLUSION
•What we did ?
Till this phase of project, we have created a front end design
using jupyter widget, and with the help of python programming
language.