AI in Healthcare
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Follow on Instagram for UpdatesAI in Healthcare Syllabus — Unit-wise Content
The official GTU curriculum for AI in Healthcare (subject code DI04003081) as prescribed for ELECTRICAL · Semester 4 divides the course into 5 units, covering 39 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (DI04003081)Unit 1 Introduction to Artificial Intelligence 05
- Intelligence: Define
- Human and Machine Intelligence. 1.2
- Artificial Intelligence (AI): Basic of AI, Strong AI, Weak AI, Reactive machines
- Limited memory
- Theory of mind
- History of Artificial Intelligence 1.4
- Need for Artificial Intelligence 1.5
- Relationship between Artificial Intelligence
- Machine Learning and Deep Learning 1.6
- Disadvantages and Future of Artificial Intelligence 1.7
- Applications of Artificial Intelligence
Unit 2 Fundamental of Artificial Neural Networks 05
- 2.1 Basic of Artificial Neural Network (ANN) 2.2 History of ANN 2.3 Biological Neuron 2.4 Architecture of an artificial neural network 2.5 Types of Artificial Neural Networks: Feedback ANN, Feed- Forward ANN 2.6 Types of Learning: Supervised
- Unsupervised and Reinforcement 2.7 Advantages of Artificial Neural Network 2.8 Disadvantages of Artificial Neural Network 2.9 Applications of Artificial Neural Networks
Unit 3 Supervised Learning Models 8
- 3.1 Introduction of Supervised Learning: Define Regression
- Real-world Applications Examples. 3.2 Steps in Supervised learning. 3.3 Types of supervised learning algorithms: 3.3.1 Regression: Linear Regression
- Regression Trees
- Non-Linear Regression
- Bayesian Linear Regression
- Polynomial Regression. 3.3.2 Classification: Random Forest
- Decision Trees
- Logistic Regression
- Support vector Machines
- Naive Bayes
- K-Nearest Neighbor. 3.4 Advantages of Supervised learning. 3.5 Disadvantages of Supervised learning. 20% Program Name: Diploma Engineering Level: Diploma Branch: Biomedical Engineering Subject Code: DI04003081 Subject Name: AI in Healthcare
Unit 4 Unsupervised Learning Models 7
- 4.1 Introduction of Unsupervised Learning: Define and Working. 4.2 Types of Unsupervised learning algorithms: Clustering and Association. 4.3 K-means for clustering problems
- Principal Component Analysis
- Hierarchal clustering
- Anomaly detection
- Neural Networks. 4.4 Advantages of Unsupervised learning. 4.5 Disadvantages of Unsupervised learning. 4.6 Difference between Supervised and Unsupervised Learning
Unit 5 Application of Artificial Intelligence in Healthcare and advance
- algorithms 5 5.1 Fuzzy Logic Systems: Introduction
- Advantages and Disadvantages. 5.2 Natural Language Processing (NLP): Introduction
- Components and Steps. 5.3 Application of AI in Healthcare: Introduction
- Traditional healthcare system
- AI-based healthcare system for Disease Identification and Diagnosis
- Application in Radiology
- Drug interaction and manufacturing
- Pre- and Post-diseases diagnosis
- Predictive Analytics and Early Warning Systems
- Remote Patient Monitoring 5.4 AI based biomedical waste management system
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