This document presents a machine learning project to detect heart disease using various algorithms. It introduces the dataset used, which contains clinical and diagnostic information on patients. The objectives are to build an accurate predictive model for heart disease classification using supervised and unsupervised learning algorithms. A flow diagram shows the project workflow of data preprocessing, model building and evaluation. The conclusion discusses the potential of machine learning and early detection to improve patient outcomes and healthcare.

1. 1
An Overview
of Computing
&
Career
Planning
UNIVERSITY INSTITUTE OF COMPUTING
Master of Computer Applications Cloud Computing & Devop’s
Minor Project
20CAR-357/ SCR-363
HEART DISEASE DETECTION USING MACHINE LEARNING ALGORITHMS
Minor Project Presentation DISCOVER . LEARN . EMPOWER
Student Name: JASPREET KAUR
UID: 22MCC20166
Section/Group: 22MCD/2A
Supervisor Name: ANJUL BHARADWAJ
SUMIT KUSHWAHA
Designation: ASSISTANT PROFESSOR

2. Presentation Outline
2
• Introduction to Project
• Dataset
• Objectives
• Technology Used
• Project Flow Diagram
• Final Output
• Conclusion & Future Aspect
• Software & Hardware Used

3. INTRODUCTION
• Heart disease is a critical health issue globally, and early detection is crucial for better
patient outcomes.
• Machine learning can play a significant role in improving heart disease detection and
risk assessment.
• Heart disease is a leading cause of death worldwide, and early detection can save lives.
• Traditional diagnosis methods can be time-consuming and costly.
• Machine learning can analyze vast amounts of patient data and assist in accurate and
timely detection.
• In this project, we will explore a comprehensive dataset comprising clinical and
diagnostic information of patients, including demographics, medical history, lifestyle
factors, and results of various cardiac tests. We will preprocess the data to handle
missing values, normalize features, and select relevant variables to ensure the quality
and integrity of the dataset.

4. DATASET

5. OBJECTIVES
• This project aims to predict future Heart Disease by analyzing data of
patients that classifies whether they have heart disease or not using the
machine-learning algorithm.
• The heart disease detection project's objectives encompass building an
accurate predictive model, utilizing diverse patient data, ensuring data
quality, evaluating performance, creating a user-friendly interface,
supporting clinical decision-making, and contributing to the advancement
of heart disease detection for better public health outcomes.

6. TECHNOLOGY USED
Different machine learning algorithms can be used for heart disease detection, such
as:
Supervised Learning Algorithms:
• Logistic Regression
• Linear Regression
• Decision Trees
• Random Forests
Unsupervised Learning Algorithms:
• Neural Networks
Semi-Supervised Learning
Reinforcement Learning

7. FLOW DIAGRAM

8. FINAL OUTPUT

9. CONCLUSION & FUTURE ASPECTS
Conclusion
• Machine learning algorithms offer a promising approach for heart disease
detection.
• Early detection can lead to timely interventions and improved patient outcomes.
• Collaborations between data scientists, medical professionals, and policymakers
are vital for successful implementation.
Future Aspects
• Continuous improvement in machine learning algorithms and data availability.
• The potential of AI to revolutionize healthcare by assisting doctors in diagnosis
and treatment planning.

10. SOFTWARE & HARDWARE USED
• Operating system: Windows 10.
• Used: Python, Jupyter Notebook and Anaconda Prompt
• Hardware: AMD Ryzen 5
• RAM: 8GB.

11. THANK YOU