This document summarizes a student project analyzing cardiovascular disease risk using bioinformatics. It introduces bioinformatics and describes how gene expression analysis and genetic algorithms can be used to study cardiovascular diseases like atherosclerosis and predict disease risk. The project uses a genetic algorithm approach involving feature selection, initial population creation, selection, crossover, and mutation to analyze a cardiovascular disease dataset and identify the individual with the best fitness.

1. Risk of Cardiovascular
disease analysis in
Bioinformatics framework
A project by:
Agnish, Anindya, Gopal, Rashbihari
IT-4th year

2. Introduction
• Bioinformatics is the use of computers for the
acquisition, management, and analysis of biological
information.
• Bioinformatics is emerging and advance branch of
biological science , contain Biology mathematics and
Computer Science.
• Bioinformatics is clearly
a multi-disciplinary field
including: computer systems
management networking,
database design, computer programming, molecular --
biology.

3. Gene Expression Analysis of Cardiovascular
Diseases
Cardiovascular diseases:-
a. Atherosclerosis
b. Atrial Fibrillation
c. Heart Failure
d. Hypertension…. etc.
Treatment through Gene Therapy:-
a. Identifying candidate Gene through RNA
profiling.
b. Cancer genomics as a template for
cardiovascular medicine:- The Cancer literature
provides us with some guidance how gene
expression data might be applied in context of
cardiovascular medicine.

4. Prediction of Genetic Algorithm(GA)
through cardiovascular disease
In the field of AI, a ‘GA’ is a search heuristic that imitates the process
of natural evolution.
Genetic algorithm belongs to larger class of evolutionary algorithms,
which generate optimized solutions using techniques inspired by
natural evolution, such as inheritance, mutation, etc. A typical
genetic algorithm requires-
(i) A genetic representation of solution domain
(ii) A fitness function to evaluate the solution domain.
=no. of features, fi= mask value of ‘i’th function;
“1” represents that feature ‘i’ is selected
“0” represents that feature ‘i’ is not selected

5. Flow Chart for GA
Heart
disease(Cardiovascul
ar) data set
Create fitness
function for GA i.e f(x)
Create an initial
population
Selection
(Parents1,Parents 2)
Crossover
Mutation
Stop
Best individual
(Child c1)
Calculate distance between the
parents and child chromosome
and consider the f(x)
Result
Load
Data(Selected
Feature)
Start
StopNo
Yes

6. References
Our respected mentor Annwesha madam helping us a lot for
doing this project. We are also getting help from the following
links…
1. www.ijltet.org
2. www.iraj.com
3. https://en.wikipedia.org/wiki/Cardiovascular_disease
4. https://orwh.od.nih.gov/sites/orwh/files/docs/ORWH-HIC-Cardiovascular-
Disease.pdf
5. www.who.int/cardiovascular_diseases/guidelines/Full%20text.pdf