Presentation on project of heart disease prediction

1. Heart Disease Prediction
system
Using Machine Learning
1
PROJECT :

2. GUIDED BY:
Prof. Upasana Mehta
Bhagwan Mahavir College of
Management(MCA)
HEAD OF DEPARTMENT:
Prof. Upasana Mehta
Bhagwan Mahavir College
of Management(MCA)

3. contents 1) Project Introduction
2) Type of Heart Disease
3) Objective
4) Risk Factor
5) Process
6) Attributes
7) Dataset
8) Machine Learnin Algorithm
9) Model Accuracy
10) Conclusion

4. Introduction
Heart disease refers to various
conditions affecting the heart and
blood vessels, including coronary
artery disease, heart attacks,
heart failure, arrhythmias, heart
valve issues, and congenital
defects.
What is Heart Disease?

5. Types
 Heart Rhythm Disorders (Arrhythmias)
 Coronary Artery Disease (CAD)
 Heart Failure
 Heart Valve Disease
 Congenital Heart Defect
 Cardiomyopathy
 Pericardial Disease
 Aortic Disease
 Deep Vein Thrombosis (DVT) and Pulmonary Embolism (PE)

6. Objective
EARLY DETECTION:
PREDICTING
INDIVIDUALS AT
HIGH RISK OF HEART
DISEASE, ALLOWING
FOR EARLY
INTERVENTION AND
PREVENTATIVE
IMPROVED
DECISION
MAKING: PROVIDIN
G DOCTORS WITH
ADDITIONAL DATA
POINTS TO SUPPORT
DIAGNOSIS AND
TREATMENT
DECISIONS.
ACCESSIBILITY: DEM
OCRATIZING ACCESS
TO THIS TYPE OF
ANALYSIS BEYOND
SPECIALIZED
SOFTWARE FOR
BROADER
APPLICATION IN
HEALTHCARE
SETTINGS.

7. Risk
factor
Age: Risk increases with age.
Family history: Having a close relative with heart
disease puts you at higher risk.
Lifestyle: Unhealthy habits like smoking, physical
inactivity, and unhealthy diet contribute significantly.
High blood pressure: Uncontrolled hypertension
strains the heart and damages blood vessels.
High cholesterol: High levels of LDL ("bad")
cholesterol promote plaque buildup.
Diabetes: Can damage blood vessels and increase
heart disease risk.

8. Process

9. Attributes:
1. Age
2. Sex
3. Chest Pain type (CP)
4. Trestbps (on admission to the hospital, resting blood pressure in mm Hg.)
5. Cholesterol
6. Fbs
7. Restecg (resting electrocardiographic results: assesses the heart's activity.)
8. Thalach (attained maximum heart rate)
9. Exang (Angina caused by exercise is a common)
10. complaint of cardiac patients, particularly when exercising in the cold)
11. Oldpeak (Exercise-induced ST depression compared to rest)
12. Slope (the curve of the ST segment of the peak activity)
13. Ca (flourosopy coloration of a lot of major vessels (0-3))
14. Thal (normal, fixed defect, reversable defect)
15. Num (the predicted attribute)

10. Dataset
1

11. 3 step process
Split the dataset
Train the
dataset
Compare
the Algos
1

12. 9

13. Data
visualization
1

14. Correlation
matrix

15. Machine
learning
Algoritham
Logistic Regression
K-Nearest Neighbour
Support Vector Machine
Decision Tree
Random Forest Algorithm
Gradient Boosting

16. Model’s
Accuracy

17. Conclusion
Machine learning techniques are being used to process raw
healthcare data on heart disease, enabling early detection
and prevention. This research focuses on the hybrid HRFLM
approach, which combines Random Forest and Linear
Method characteristics. The results show high accuracy in
predicting heart disease, highlighting the need for future
research to incorporate diverse machine learning
techniques and develop new feature selection methods for
improved heart disease prediction.

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