Heart Attack Prediction using artifical intelligence

Heart Attack Prediction PPT.pptx

1. HEART ATTACK PREDICTION CAPSTONE PROJECT Presented By: 1. S.GUNAPRIYA -JP College of Engineering-CSE

2. OUTLINE    Problem Statement Proposed System/Solution System Development Approach   Algorithm & Deployment   Result Conclusion Future Scope References

3. Problem Statement Example:   Predicting heart attacks accurately is essential to reducing mortality and enhancing patient care. Predictive techniques used today frequently lack early detection and accuracy. Artificial Intelligence (AI) presents a promising means of improving forecast accuracy through multidimensional, complicated data analysis. To improve overall cardiovascular health management, lower death rates, and provide timely therapies, it is imperative to develop strong AI models for heart attack prediction

4. Proposed Solution Predicting heart attacks involves analyzing a combination of risk factors and symptoms to identify individuals at high risk. Here’s a comprehensive approach to creating a solution for heart attack prediction : 1. DATA COLLECTION Medical History : Previous heart conditions, family history of heart disease, and other health conditions. Demographics : Age, gender, ethnicity. Lifestyle Factors: Smoking status, physical activity level, diet. 2 . DATA INTEGRATION AND PREPROCESSING Data Cleaning: Handle missing values, outliers, and inconsistencies in the data. Normalization: Scale data to ensure uniformity, especially for machine learning models. Feature Engineering: Create new features from existing data that might be predictive of heart attacks . 3. PREDICTIVE MODELING Logistic Regression: For binary classification of risk (high vs. low). Decision Trees and Random Forests: To capture non-linear relationships and interactions between features. Gradient Boosting Machines (GBMs): For improved accuracy and handling of complex patterns.

5. System  Approach Develop a heart attack prediction system by integrating real-time clinical and wearable data with advanced machine learning models to provide accurate risk assessments and actionable insights. Ensure seamless user interaction, data security, and continuous model refinement to enhance predictive accuracy and patient care . Create a heart attack prediction system using real-time data from clinical records, analyzed by advanced machine learning models for accurate risk assessment. Ensure secure data handling and actionable insights for users and healthcare providers . Implement a heart attack prediction system by integrating real-time clinical and wearable data with machine learning models to assess risk accurately. Ensure user-friendly interfaces and robust data security.

6. Algorithm & Deployment Algorithm : Employ a Random Forest classifier to predict heart attack risk by analyzing patient data, including age, blood pressure, cholesterol levels, and lifestyle factors. Random Forests handle complex, high-dimensional data and provide feature importance insights . Deployment : Deploy the Random Forest model through a secure web or mobile application that enables users to enter their health information and receive a risk assessment. Ensure the application adheres to data privacy regulations and is updated with new medical research and user feedback.

7. Result Accuracy of 85% suggests the model is generally effective in predicting both heart attack and non-heart attack cases. Precision of 80% indicates a relatively low rate of false positives, which means fewer individuals are incorrectly flagged as high risk. Recall of 90% shows the model is very good at identifying actual heart attack cases, though there may still be some missed cases. F1 Score of 85% balances precision and recall, reflecting the model’s overall effectiveness in identifying heart attack risks. AUC-ROC of 0.92 indicates strong ability to differentiate between high and low-risk individuals. These metrics provide a comprehensive view of the model’s performance, helping you understand its accuracy and effectiveness in predicting heart attack risks. .

8. Conclusion A heart attack prediction system, utilizing real-time clinical and wearable data analyzed through advanced machine learning models, offers significant promise for enhancing cardiovascular health outcomes. By integrating accurate predictive algorithms with user-friendly interfaces and robust data security measures, such systems can effectively identify individuals at high risk for heart attacks. Key performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC demonstrate the model’s effectiveness in distinguishing between high and low-risk cases. In practice, such a system not only aids in early detection and prevention but also supports personalized healthcare by providing actionable insights and timely alerts. Continuous model refinement and adaptation to new data ensure that the system remains reliable and effective, contributing to improved patient care and potentially reducing the incidence of heart attacks.

9. Integration of Genetic Data: Incorporate genetic information for more personalized risk assessments . Advanced AI Techniques: Explore advancements in AI, such as deep learning and reinforcement learning, to improve prediction accuracy. Behavioral Insights: Utilize insights from behavioral data to offer tailored lifestyle and preventive recommendations. . Future scope

10. References Risk Scores : Tools like the Framingham Risk Score or ASCVD Risk Calculator use basic health information (like age, cholesterol, blood pressure, and smoking status) to estimate your risk of having a heart attack over the next 10 years . Genetic Information : Scientists are studying how our genes affect heart attack risk. This research might help personalize predictions based on individual genetic information in the future . Guidelines : Organizations like the American Heart Association (AHA) provide guidelines on assessing and managing heart attack risk, including lifestyle recommendations and medical treatments. These methods and tools help doctors estimate your risk and guide prevention or treatment.

11. THANK YOU