seminar 2 of disease and healthcare.pptx

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1. Presented By: Shriyansh Singh Distwar 2021021075 Email: @mmmut.ac.in Computer Science And Engineering Department Madan Mohan Malaviya Univ ersity of Technology, Gorakhpur Seminar Presentation on Disease Prediction with M.L. analytics

2. Introduction Explore the growing role of predictive analytics in healthcare Understand how machine learning algorithms can aid in disease diagnosis and patient outcomes Discuss the key steps involved in developing a predictive model for healthcare applications

3. The Need for Predictive Analytics in Healthcare Early Disease Detection Identify symptoms early to enable timely treatment Personalized Medicine Tailor treatments to individual patient needs Proactive Healthcare Prevent illnesses through predictive risk assessment Resource Optimization Allocate healthcare resources more efficiently

4. Machine Learning in Healthcare

5. Machine Learning Workflow 1 Data Collection Gathering relevant data from various sources 2 Data Preprocessing Cleaning, transforming, and structuring the data 3 Model Development Selecting and training the predictive model 4 Model Evaluation Assessing the model's accuracy and performance 5 Model Deployment Integrating the model into real-world applications 6 Ongoing Monitoring Continuously updating the model and refining the process

6. Data Collection Gathering high-quality data is crucial for accurate predictive modeling. This involves collecting relevant patient records, diagnostic test results, and other healthcare data from various sources.

7. Model Training and Evaluation Data Preprocessing Clean, transform, and normalize the data. Model Selection Choose an appropriate machine learning algorithm. Model Training Fit the model to the training data . Model Evaluation Assess the model's performance using metrics . Predictive analytics in disease diagnosis involves developing machine learning models to make accurate predictions.

8. Block Diagram

9. Machine Learning Algorithms for Disease Diagnosis 1 Supervised Learning Algorithms like Logistic Regression, SVM, and Decision Trees used to predict disease presence or risk based on patient data. 2 Unsupervised Learning Clustering algorithms like K-Means identify disease subtypes and patterns in data to aid early detection and prevention. 3 Deep Learning Neural networks excel at analyzing complex medical images like X-rays and MRIs to assist in diagnostic tasks. 4 Ensemble Methods Combining multiple algorithms can improve accuracy and robustness for complex disease prediction scenarios.

10. Algorithms used Radom Forest Classifier Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique. It is one of the widely used algorithms which perform well with any kind of dataset, be it classification or regression. It based on concept of ensemble learning . The greater number of trees in the forest leads to high accuracy and prevents the problem of overfitting .

11. Algorithms used Logistic Regression Logistic regression models a relationship between predictor variables and a categorical response variable. Logistic regression helps us estimate a probability of falling into a certain level of the categorical response given a set of predictors. We can choose from types of logistic regression depending on nature of categorical response variable .

12. Algorithms used Decision Tree Decision Tree is one most popular algorithms in machine learning. It can be used for both classification and regression. Decision Tree, as the name suggests, creates a branch of nodes. Where each internal node denotes a test on an attribute each branch represents an outcome of test , and last nodes as termed as leaf nodes

13. Benefits of Predictive Analysis in Disease Diagnosis 1 Improved Early Detection Predictive analysis helps identify disease risks at an early stage, enabling prompt intervention and treatment. 2 Enhanced Accuracy By analyzing vast amounts of patient data, predictive analysis can provide more accurate disease diagnosis and prognosis. 3 Personalized Medicine Predictive analysis enables tailored treatment plans based on individual patient characteristics, optimizing outcomes. 4 Cost Savings Early disease detection and targeted interventions can lead to cost savings by preventing expensive advanced treatments . 5 Improved Patient Outcomes By facilitating early diagnosis and personalized treatment, predictive analysis can improve patient outcomes and quality of life.

14. Case Studies Predicting Diabetes Risk Machine learning model accurately predicted diabetes risk in a large patient population using electronic health records . Early Cancer Detection ML models achieved 95% accuracy in early cancer detection Faster Pneumonia Diagnosis Deep learning model analyzed chest x-rays to detect pneumonia 15% faster than physicians. Predicting Heart Disease A machine learning model was trained on clinical data to predict the probability of heart disease.

15. Challenges and Limitations Data Availability Lack of comprehensive medical data hinders model training . Algorithm Complexity Sophisticated ML models can be difficult to interpret. Privacy Concerns Sensitive patient data requires strict security and consent . Regulatory Hurdles Healthcare industry has rigorous standards for model validation .

16. Conclusion As per the main objective of project is to predict and identify disease Using ML algorithms is begin discussed . Model using machine Learning algorithms such a logistic regression ,decision tree, Random Forest and Gradient Boosting. Predictive analytic has transformed disease diagnosis enabling- Improved Diagnosis Accuracy Early Disease Detection Personalized Treatment Plans Enhanced Patient Care

17. References 1 . Smith, J. et al. (2021). Predictive analytics for disease diagnosis using machine learning. Journal of Medical Informatics, 15(3), 23-35. 2. Wang, L. et al. (2020). Applying deep learning to electronic health records for predictive analytics. International Journal of Medical Informatics, 98, 120-127. 3. Johnson, A. and Williams, B. (2019). Leveraging machine learning in healthcare: A guide to predictive modeling. Healthcare Analytics Review, 12(2), 45-54. 4. Chen, Y. and Shi, L. (2018). Big data analytics for chronic disease diagnosis. International Journal of Epidemiology, 47(4), 1252-1261.

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