Cardiovascular Detection using ML Techniques

This presentation provide the introduction of cardiovascular diseases.

1. CONTENTS Abstract Introduction Literature survey Existing model vs Proposed model Flow chart References

2. ABSTRACT Cardiovascular diseases remain a leading cause of mortality worldwide, and early risk prediction plays a crucial role in prevention and timely intervention. In this work, we present an intelligent system that predicts an individual’s likelihood of developing cardiovascular complications using deep learning techniques. The proposed model integrates multiple clinical parameters—such as age, blood pressure, cholesterol levels, and lifestyle factors—to capture complex, non-linear relationships that traditional statistical methods often overlook. We employed a deep neural network architecture, trained and validated on publicly available healthcare datasets, to achieve robust prediction accuracy. The system not only delivers a binary risk classification but also provides a probabilistic score, offering healthcare professionals better insight into a patient’s condition. Our experiments demonstrate improved performance compared to conventional machine learning models, with notable gains in precision and recall, making the approach suitable for real-world clinical decision support. Furthermore, the framework is designed for easy deployment as a web-based application, ensuring accessibility for both medical practitioners and individuals seeking preventive health assessment.

3. INTRODUCTION Cardiovascular diseases are one of the biggest health challenges today, affecting millions of people around the world. Detecting the risk at an early stage can save lives by allowing timely medical care and lifestyle changes. Traditional methods like statistical models and basic machine learning approaches provide some insights, but they often struggle to handle the complexity of multiple health and lifestyle factors. In this project, we aim to build an intelligent cardiovascular risk prediction system using deep learning. The model takes clinical and lifestyle information—such as blood pressure, cholesterol, glucose levels, and habits like smoking or alcohol consumption—and learns patterns that can indicate whether a person is at high or low risk. Unlike older methods, our upgraded approach not only improves accuracy but also explains which factors contribute most to the prediction. To make it practical and user-friendly, we integrate the system into a simple software interface so that healthcare professionals and individuals can easily use it for preventive care.

4. LITERATURE SURVEY S.NO AUTHORS TITLE OF THE PAPER Journal/Conference Paper V ol no , I ssue no , ISSN/ISBN MON-YEAR 1 HIRA KHAN, NADEEM JAVAID AND SHERAZ ASLAM , TARIQ BASHIR, MARIAMAKBAR, NABIL ALRAJEH Heart Disease Prediction Using Novel Ensemble and Blending Based Cardiovascular Disease Detection Networks: EnsCVDD -Net and BlCVDD -Net IEEE 12 1 July 2024 2 A Angel Nancy,Dakshanamoorthy Ravindran,P M Durai Raj Vincent,Kathiravan   IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning Electronics 11 22 July 2022

5. S.NO AUTHORS TITLE OF THE PAPER Journal/Conference Paper V ol no , I ssue no , ISSN/ISBN MON-YEAR 3 Sudarshan Nandy ,  Mainak Adhikari An intelligent heart disease prediction system based on swarm-artificial neural network Springer Nature Link 35 5 27 May 2021 4 Sofia Mary Vincent Paul ,  Sathiyabhama Balasubramaniam Intelligent Framework for Prediction of Heart Disease using Deep Learning Springer Nature Link 47 2021 5 Safial Islam Ayon Coronary Artery Heart Disease Prediction: A Comparative Study of Computational Intelligence Techniques IETE Journal of Research 68 2020

6. EXISTING MODEL VS PROPOSED MODEL EXISTING MODEL PROPOSED MODEL 1. Most models achieve low accuracy. 1. Improved accuracy through optimized deep learning architecture. 2. Relies heavily on handcrafted features. 2. Uses automated feature engineering strategy to reduce manual dependency. 3. Models have limited interpretability 3. Introduces Explainable AI (XAI) to improve transparency and trust. 4. Struggles to adapt well across different datasets. 4. Better generalization with automated feature learning and robust deep networks.

7. FLOW CHART [CVD Database] ↓ [Data Preprocessing] (Cleaning & Transformation) ↓ [Exploratory Data Analysis (EDA)] ↓ [Data Splitting / Partition] ↓ ↓ [Training Dataset] [Testing Dataset] ↓ ↓ [Deep Neural Network] ←─────── ↓ [Trained Model] ↓ [Prediction of CVD] ↓ [Performance Analysis]

8. REFERENCES https://ieeexplore.ieee.org/abstract/document/10577964 https://www.mdpi.com/2079-9292/11/15/2292 https://link.springer.com/article/10.1007/s00521-021-06124-1 https://link.springer.com/article/10.1007/s13369-021-06058-9 https://www.tandfonline.com/doi/abs/10.1080/03772063.2020.1713916

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