Heart Disease Prediction PPT

1. Heart Disease
Prediction

2. Abstract/Area of Domain
 With big data growth in biomedical and healthcare communities,
accurate analysis of medical data benefits early disease
detection, patient care and community services.
 Different regions exhibit unique characteristics of certain regional
diseases, which may weaken the prediction of disease outbreaks.
 In this paper, we streamline machine learning algorithm for
effective prediction of chronic disease.
 To overcome the difficulty of incomplete data, we use a latent
factor model to reconstruct the missing data.
 We propose a new convolutional neural network based multimodal
disease risk prediction (CNN-MDRP) algorithm using structured
and unstructured data from hospital.

3. Objective
 Today, heart failure diseases affect more people worldwide than other
autoimmune conditions. Cardiovascular Diseases (CVDs) affect the heart
and obstruct blood flow through the blood vessels. Chronic ailments in CVD
include heart disease (heart attack), cerebrovascular diseases (strokes),
congestive heart failure, and many more pathologies. Worldwide, CVDs kill
around 17 million a year, and death rates due to heart diseases have
increased after the COVID-19 pandemic.

4. Problem Definition
 Existing scheme has some defects.
 data set is typically small, for patients and diseases with
specific conditions
 the characteristics are selected through experience. these
pre-selected characteristics maybe not satisfy the changes
in the disease and its influencing factors.
 For patient’s examination data, there is a large number of
missing data due to human error.
 Existing System uses only unstructured text data
 to predict whether the patient is at high-risk of cerebral
infarction.

5. Motivation
 Several risk factors for manual heart disease prediction
may include inactivity in a physical form, unhealthy eating
habits, or even the consumption of alcohol. Preexisting
conditions, age, chest pain level, blood test results, and
several such factors can be ensemble together
computationally for heart disease prediction.

6. Introduction
 The healthcare problem of chronic diseases is also very important in
many countries.
 With the development of big data analytics technology, more attention
has been paid to disease prediction from the perspective of big data
analysis, various researches have been conducted by selecting the
characteristics automatically from a large number of data to improve the
accuracy of risk classification.
 we combine the structured and unstructured data in healthcare field to
assess the risk of disease.
 The goal is to predict whether a patient is amongst the cerebral infarction
high-risk population according to their medical history.
 A novel CNN-based multimodal disease risk prediction (CNN-MDRP)
algorithm for structured and unstructured data. The disease risk model is
obtained by the combination of structured and unstructured features.

7. Architecture Diagram

8. Existing System
 Qiu et al. proposed an optimal big data sharing algorithm to handle the
complicate data set in telehealth with cloud techniques.
 One of the application is to identify high-risk patients which can be utilized to
reduce medical cost since high-risk patients often require expensive
healthcare.
 It innovatively brought forward the concept of prediction-based healthcare
applications, including health risk assessment.

9. Proposed System
 Dataset collection is collecting data which contains patient details.
Attributes selection process selects the useful attributes for the
prediction of heart disease.
 After identifying the available data resources, they are further
selected, cleaned, made into the desired form. Different
classification techniques as stated will be applied on
preprocessed data to predict the accuracy of heart disease.
 Accuracy measure compares the accuracy of different classifiers.

10. Proposed System

11. Feasibility Study
 In this paper, we have checked the following study to be
true:
 Technical Feasibility
 Operational Feasibility
 Economical Feasibility
 Social Feasibility
 Management Feasibility
 Legal Feasibility
 Time Feasibility

12. SRS
 H/W System Configuration: -
 Processor - Pentium –III/intel i3,i5,i7
 RAM - 1 GB (min)
 Hard Disk - 20 GB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse
 Monitor - SVGA

13. SRS
 S/W System Configuration:-
 Operating System : Windows 8/8.1/10
 Application Server : Tomcat7.0/8.X
 Front End : HTML, Jsp
 Scripts : JavaScript.
 Server side Script : Java Server Pages.
 Database : MySQL
 Database Connectivity : JDBC

14. Literature Survey
Sr.
no.
Public
ation
Author
Name
Paper Name Year Objective Limitation
1 MIS
Quarterly
Hsinchun
Chen
Roger H. L.
Chiang
Veda C.
Storey
BUSINESS
INTELLIGENCE
AND ANALYTICS:
FROM BIG DATA TO
BIG IMPACT
2012 To serve, in part, as a
platform and
conversation guide
for examining how
the IS discipline can
better serve the needs
of business decision
makers in light of
maturing and
emerging BI&A
technologies,
ubiquitous Big
Data, and the
predicted shortages of
data-savvy managers
and
of business
professionals with
deep analytical skills.
Need of new
analytical skills for
Business
Intelligence.
2 Human
Language
Technologies
Wenpeng Yin
and Hinrich
Sch¨ utze
Convolutional
Neural Network for
Paraphrase
Identification
2015 To present a new deep
learning architecture
Bi-CNN-MI for
paraphrase
Bi-CNN-MI not able
to match the
sentence, question
answering and other

15. Literature Survey
Sr.
no.
Public
ation
Author
Name
Paper
Name
Ye
ar
Objective Limitation
3. Joel
Brooks,
Matthew
Kerr,
Matthew
Kerr
Joel Brooks
Matthew
Kerr,
John Guttag
Developing a Data-
Driven Player
Ranking in Soccer
Using
Predictive Model
Weights
2016 Present a novel method of
utilizing
soccer event data to
understand the relationship
between
pass location and shot
opportunities. And predict
whether a possession will end
in a shot.
Sequential in-
formation does not
give a more detailed
understanding of
how
passing strategy
relates to outcomes.
4 IEEE Senjuti Basu
Royy, Ankur
Teredesaiy,
Kiyana
Zolfaghary,
Rui Liuy,
David
Hazely?,
Stacey
Newmany,
Albert
Marinez
Dynamic
Hierarchical
Classification for
Patient
Risk-of-
Readmission
2015 Provides framework to
clearly outperforms baseline
solutions for congestive heart
failure (CHF). DHC
automatically discovers and
defines the
layers by leveraging the
underlying historical patient
data.
Different data
distributions and
classifiers at each
layer can lead
to different
probability
distribution which
may cause some
inconsistency in final
predictions.

16. Literature Survey
Sr.
no.
Public
aton
Author
Name
Paper
Name
Year Objective Limitation
5 IEEE Min Chen,
Ping Zhou,
and
Giancarlo
Fortino
Emotion
Communication
System
2016 emotion
communication protocol,
which provides a high-
level reliable
support for the
realization of emotion
communications.
There is delay in
two modes in
emotion
communication
system,

17. Project Plan