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ABSTRACT
The significance of the heart as the body’s most vital organ cannot be stressed. Heart
disease is the leading cause of death worldwide. Heart failure (HF) is a main cause of
death that must be successfully predicted (HF). Angiography, the gold standard for
clinical diagnosis of HF, is expensive and can have catastrophic repercussions,
according to research. In this scenario, machine learning and deep learning are
applied. Machine learning and deep learning techniques can be used to forecast the
whole range of hazards associated with this project. This dataset is created by
combining previously available datasets with eleven distinct categories. This
investigation would not be possible without this information. According to the findings,
machine learning approaches exceeded deep learning in the diagnosis of
cardiovascular diseases. We utilized the PCA approach to estimate the relative
relevance of each of the dataset’s 11 fields. When sample approaches were applied,
accuracy and recall rates increased. According to the data, Random Forest
Classifiers, Decision Tree Classifiers, and Nave Bayes algorithms surpass other ML
algorithms.
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TABLE OF CONTENTS
CHAPTER.NO
TITLE
PAGE.NO
ABSTRACT
v
LIST OF FIGURES
viii
ABBREVIATIONS
x
1
INTRODUCTION
1
1.1
HEART DISEASES
1
1.2
WHAT IS MACHINE LEARNING
4
2
LITERATURE SURVEY
8
2.1
PREDICTION OF HEART DISEASE USING
MACHINE LEARNING ALGRITHMS
8
2.1
EFFECTIVE HEART DISEASE PREDICTION USING
HYBRID MACHINE LEARNING TECHNIQUES
9
2.3
APPLICATION OF MACHINE LEARNING IN
DISEASES
9
2.4
CLASSIFICATION OF HEART DISEASE USING
K-NEAREST NEIGHBOR AND GENETIC
ALGORITHM
10
2.5
EARLY AND ACCURATE PREDICTION OF HEART
DISEASE USING MACHINE LEARNING MODEL
10
3
AIM AND SCOPE OF THE INVESTIGATION
11
3.1
MOTIVATION
11
3.2
PROBLEM DEFINITION
11
3.3
OBJECTIVE OF THE PROJECT
12
3.4
EXISTING SYSTEM
12
3.5
PROPOSED SYSTEM
13
3.6
LIMITATIONS OF PROJECT
13
3.7
FEASIBILITY STUDY
14
3.8
SYSTEM REQUIREMENTS
14
3.9
SYSTEM ARCHITECTURE DIAGRAM
15
4
MODULES AND ALGORITHMS
16
vii
4.1
DESCRIPTION OF THE DATASET
16
4.2
DATA PREAPARATION
17
4.3
MACHINE LEARNING CLASSIFIERS PROPOSED
18
4.4
DEEP LEARNING ALGORITHMS PROPOSED
24
4.5
UML DIAGRAMS
26
5
RESULT AND DISCUSSION PERFORMANCE
ANALYSIS
34
5.1
PERFORMANCE METRICS
35
5.2
PERFORMANCE ANALYSIS
35
6
SUMMARY AND CONCLUSIONS
38
REFERENCES
39
APPENDIX
A. SCREENSHOTS
41
B. SOURCE CODE
43
