Diabetes_Prediction_System_Presentation.pptx

Predict Diabetes using given Dataset

1. Diabetes Prediction System Leveraging Machine Learning for Early Detection Your Name and Date

2. Introduction What is Diabetes? - Brief introduction to diabetes (Type 1, Type 2) - Importance of early detection and prevention

3. Objective Purpose of the System: - Predict the likelihood of diabetes based on medical parameters - Help in early diagnosis and improving treatment

4. Dataset Data Source: PIMA Indian Diabetes Database or similar Attributes: - Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Age, etc. Sample Data Visualization (Charts or tables showing the dataset)

5. Methodology Data Preprocessing: - Handling missing values - Feature scaling Algorithm Used: - Logistic Regression, Random Forest, Decision Tree, or Neural Networks

6. Machine Learning Model Steps: 1. Data Collection 2. Data Preprocessing 3. Model Training 4. Model Evaluation 5. Prediction

7. Model Training Training the Model: - Train-Test Split (80%-20%) - Algorithm used (e.g., Logistic Regression or Random Forest) Tools Used: - Python, Scikit-Learn, TensorFlow (if applicable)

8. Evaluation Metrics Metrics to Evaluate the Model: - Accuracy - Precision - Recall - F1 Score - Confusion Matrix (include a graphical confusion matrix)

9. Results Model Accuracy: Display performance metrics like accuracy score Graphical Representation: Include charts/graphs to show model performance

10. Future Scope Improvements: - Integration with IoT devices - Real-time prediction using continuous glucose monitors - Expanding dataset and features

11. Conclusion Key Takeaways: - Benefits of machine learning in medical diagnostics - Potential for widespread use in healthcare systems

12. Q&A Questions and Answers Session