This repository presents a diverse portfolio of healthcare AI/ML projects focused on predictive analytics, disease diagnosis, medical imaging, claims analytics, insurance cost prediction, fraud detection, NLP/LLMs, and scalable end-to-end deployment using modern MLOps and cloud-based CI/CD pipelines.

These projects span a wide range of healthcare challenges, including:

• Predictive modeling and risk stratification for chronic diseases (e.g., cardiovascular disease, diabetes, cancer, and CMS/Medicare-Medicaid analytics)
• Medical insurance cost prediction, claims analytics, and healthcare survey analysis
• NLP/LLM applications for clinical documentation, medical transcription, patient interaction, RAG-based retrieval, and medical Q&A
• Deep learning for medical imaging, including classification, segmentation, and anomaly detection
• End-to-end scalable AI/ML systems powered by MLOps, CI/CD pipelines, cloud deployment, and automated workflows

Projects utilize a blend of structured and unstructured healthcare datasets, such as:

• Electronic Health Records (EHRs)
• Clinical trial and insurance claims data
• Wearable sensor signals (e.g., photoplethysmogram/PPG)
• Public health and demographic surveys (e.g., BMI datasets)
• Unstructured clinical notes and medical text corpora
• Public CMS Datasets
• Kaggle

• Cancer Classification – Binary classification for malignant vs. benign tumors
• Cardiovascular Disease Risk – Risk scoring using demographic and clinical features
• Wearable Activity Monitoring – Classification of physical activity from wearable PPG data

• RAG & Information Retrieval – Semantic search and question answering using vector databases and transformer embeddings
• LLM FineTuning - PEFT (LoRA/QLoRA), RoLA, and OpenAI/Hugging Face fine-tuning for clinical text analytics
• Clinical Text Processing – Medical text summarization, translation, and entity extraction using T5, BioBERT, ClinicalBERT, and AutoModelForSeq2SeqLM
• Conversational AI – Symptom-based healthcare chatbots and medical Q&A assistants
• Agentic AI - Multi-agent healthcare assistants with reasoning, tool use, and multimodal capabilities (text/image/chatbot)
• LLM Deployment & MLOps - FastAPI, Streamlit, Docker, MLflow, GitHub Actions, and AWS/Azure-based deployment pipelines
• Multimodal AI - Integration of medical imaging, clinical text, and EHR data using ClinicalBERT, CNN/ResNet, and multimodal fusion models
• LLM Deployment & MLOps - FastAPI, Streamlit, Docker, MLflow, GitHub Actions, and AWS/Azure deployment pipelines

• Automated ML lifecycle with CI/CD pipelines and airflow
• Model tracking, versioning, and reproducibility with tools like MLflow
• Scalable deployment using containerization and cloud orchestration

• COVID-19 X-ray Diagnosis – CNN-based classification using VGG, DenseNet, Inception
• Medical Image Segmentation – Semantic/instance segmentation with U-Net (2D/3D)
• Brain Tumor Detection (MRI) – Region detection using ResNet
• Tissue Segmentation – Object detection and instance segmentation via YOLOv8

• Insurance Cost Prediction – Multivariate Linear regression models for estimating medical expenses
• BMI & Health Risk Estimation – Predictive modeling from public health surveys

• Mortality Risk Prediction
  Building and evaluating machine learning and ensemble models to predict short- and long-term patient outcomes using EHR, claims, and clinical data, enabling early intervention and risk stratification.

• Chronic Disease Identification
  Detecting chronic conditions and high-risk patient populations through supervised learning, anomaly detection, and unsupervised clustering techniques for proactive healthcare management.

• Healthcare Cost Forecasting
  Estimating healthcare utilization, medical costs, and insurance premiums based on demographic, lifestyle, claims, and clinical indicators to support financial planning and payment optimization.

• Fraud, Waste, and Abuse (FWA) Detection
  Developing AI/ML and anomaly detection models to identify suspicious claims, billing irregularities, provider outliers, and potentially fraudulent healthcare activities using CMS and insurance claims data.

• Patient Segmentation and Risk Stratification
  Applying clustering, statistical modeling, and predictive analytics to group patients by utilization patterns, disease severity, and treatment risks for personalized healthcare strategies.

• Clinical Text Analytics and NLP/LLM Applications
  Leveraging NLP, RAG pipelines, and LLM-based solutions to analyze clinical notes, insurance documents, and unstructured healthcare text for information retrieval, summarization, and conversational AI applications.

• MLOps and CI/CD Automation
  Designing scalable end-to-end MLOps workflows with GitHub Actions, Docker, AWS, MLflow, and automated testing pipelines for reproducible model training, deployment, and monitoring.

• Explainable AI and Model Interpretability
  Integrating explainability techniques such as SHAP, feature importance analysis, and interpretable statistical modeling to support transparent and trustworthy healthcare AI systems.

For detailed, large-scale projects involving sensitive or proprietary healthcare datasets, access is available upon request via the private repository:
Projects of Big Data for Healthcare

• Hadoop Ecosystem

  • Tools: MapReduce, Hive, Pig
  • Use Cases: Distributed ETL, feature engineering, and batch aggregation

• Apache Spark (Scala)

  • Capabilities: RDD-based workflows for parallel data processing and pipeline orchestration
  • Libraries: MLlib for scalable machine learning on large datasets