You will get AI/ML Models for Medical Anomaly Detection in Keras, Scikit-Learn & PySpark
Project details
Are you looking to identify anomalies in medical data, detect irregularities in health records, or build intelligent systems for rare event detection?
I will help you build custom AI/ML solutions for medical anomaly detection using powerful frameworks like Keras, Scikit-Learn, and PySpark â tailored for clinical, insurance, or research applications.
đ¨ââď¸ Use Cases I Support:
Detection of abnormal patient records or lab results
Outlier detection in EHR/EMR datasets
Identifying rare disease patterns
Healthcare fraud detection
Medical time-series anomaly detection
âď¸ Technologies I Use:
Keras for deep learning (autoencoders, LSTM, etc.)
Scikit-Learn for traditional ML (Isolation Forest, SVMs)
PySpark for big data handling & scalable ML pipelines
Preprocess medical data (handle missing values, imbalances, encoding)
Engineer features based on domain knowledge
Build & tune ML/DL models for anomaly detection
Evaluate model performance (Precision, Recall, ROC, AUC, AUPRC)
Deliver deployable code or model files (optional: REST API integration)
Clean, modular source code (Python)
Trained ML/DL model (e.g., .h5, .pkl, or Spark model)
Evaluation report (PDF or Jupyter Notebook)
I will help you build custom AI/ML solutions for medical anomaly detection using powerful frameworks like Keras, Scikit-Learn, and PySpark â tailored for clinical, insurance, or research applications.
đ¨ââď¸ Use Cases I Support:
Detection of abnormal patient records or lab results
Outlier detection in EHR/EMR datasets
Identifying rare disease patterns
Healthcare fraud detection
Medical time-series anomaly detection
âď¸ Technologies I Use:
Keras for deep learning (autoencoders, LSTM, etc.)
Scikit-Learn for traditional ML (Isolation Forest, SVMs)
PySpark for big data handling & scalable ML pipelines
Preprocess medical data (handle missing values, imbalances, encoding)
Engineer features based on domain knowledge
Build & tune ML/DL models for anomaly detection
Evaluate model performance (Precision, Recall, ROC, AUC, AUPRC)
Deliver deployable code or model files (optional: REST API integration)
Clean, modular source code (Python)
Trained ML/DL model (e.g., .h5, .pkl, or Spark model)
Evaluation report (PDF or Jupyter Notebook)
Machine Learning Tools
ChatGPT, Google AutoML, GPT-3, Keras, Microsoft Power BI, OpenCV, pandas, PyMC, Python, Python Scikit-Learn, PyTorch, QlikView, R, RapidMiner, SAS, scikit-learn, SciPy, SPSS, SQL, Stata, Tableau, TensorFlow, Vertex AI, Weka, XGBoostWhat's included
| Service Tiers |
Starter
$15
|
Standard
$20
|
Advanced
$25
|
|---|---|---|---|
| Delivery Time | 3 days | 2 days | 2 days |
Number of Revisions | 2 | 3 | 4 |
Number of Model Variations | 2 | 3 | 4 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Frequently asked questions
About Aashiq Ali
Data Science, Machine Learning, AI/ML Consultant & Automation
Karachi, Pakistan - 11:16 am local time
What I offer
AI/ML Development: End-to-end machine learning solutions for classification, regression, and anomaly detection
Generative AI: Advanced image generation pipelines using Stable Diffusion and Flux
LLM & RAG Systems: Deployment and fine-tuning of large language models with retrieval-augmented generation
AI Agent Workflows: Automation solutions using LangChain, CrewAI, and n8n
Multi-modal AI: Integration of text, image, and tabular data processing
Development Capabilities
Backend Frameworks: Django, Flask, FastAPI
Frontend Technologies: React, Streamlit, Gradio
Mobile Development: Flutter (Cross-Platform), React Native, Native iOS/Android
Cloud Platforms: AWS, Azure, Google Cloud
đ Service Offerings
AI & Machine Learning Solutions
Custom ML/DL models for classification, regression, and anomaly detection
Computer vision solutions for object detection and image segmentation
Generative AI tools for high-quality content creation
AI agents for real-time task automation
Customizable chatbots with context retention
Generative AI & Image Generation
Image generation pipelines using Stable Diffusion and Flux
Virtual try-on systems with Stable Diffusion inpainting
Creative content generation solutions
Integration of generative models into chatbots
Large Language Models & RAG
LLM deployment and fine-tuning for conversational agents
Retrieval-Augmented Generation (RAG) systems
Multi-modal LLM solutions (text, images, tabular data)
Custom AI agents using LangChain
Chatbot Development & Integration
Intelligent chatbots with personalized conversations
Context-aware dialogue systems
CRM and cloud API integrations
End-to-end chatbot systems deployment
AI-Driven Document Solutions
Advanced OCR systems for data extraction
Intelligent document parsing models
OCR integration with chatbots for query resolution
Cloud & API Development
Cloud-based AI model deployment
Custom API development for AI integration
Containerized solutions using Docker
đ Industry Applications
Real Estate
Lead qualification and client concierge agents
Property recommendation systems
Automated scheduling and CRM integration
Healthcare
HIPAA-compliant telemedicine solutions
Patient onboarding and support automation
Medical document processing
E-Commerce
Customer support automation
Returns processing systems
Loyalty program integration
Education & EdTech
Student onboarding automation
Intelligent tutor agents
Educational content processing
B2B SaaS
Customer onboarding workflows
Analytics and reporting systems
Billing and subscription management
âď¸ Technical Stack
AI & Machine Learning
Frameworks: TensorFlow, PyTorch, Scikit-learn
LLMs: GPT-4, Claude, Gemini, Custom LLMs
Computer Vision: OpenCV, Stable Diffusion, FLUX
Automation: LangChain, CrewAI, n8n
Development Tools
Backend: Django, Flask, FastAPI, Node.js
Frontend: React, Next.js, Streamlit, Gradio
Mobile: Flutter, React Native
Databases: PostgreSQL, Supabase, Firestore, Airtable
Cloud & Deployment
Platforms: AWS, Azure, Google Cloud
Containerization: Docker, Kubernetes
CI/CD: GitHub Actions, GitLab CI
Integration & Messaging
Communication: WhatsApp Business API, Twilio, Intercom
CRM: HubSpot, Zoho, GoHighLevel, Pipedrive
Payments: Stripe, PayPal
đ Project Delivery Approach
Phase 1: Discovery & Scoping
Requirements gathering and analysis
Clear goal definition and KPI establishment
Project timeline and resource planning
Phase 2: Pilot Development (2-6 weeks)
Rapid prototyping and iterative development
Regular client feedback incorporation
Minimum viable product delivery
Phase 3: Testing & Deployment
Comprehensive testing and quality assurance
Team training and knowledge transfer
Production deployment and monitoring
Phase 4: Scaling & Optimization
Performance optimization
System scaling for increased load
Continuous improvement implementation
Phase 5: Documentation & Handover
Comprehensive technical documentation
Operational runbooks and support guidelines
Post-deployment support planning
đź Case Studies
AI CRM Integration
"Built ChatGPT agent integrated with CRM system"
"WhatsApp bot with menu and payment capabilities"
"AI phone agent for call booking and HubSpot logging"
Document Processing
"RAG implementation for document-based query answering"
"PDF and Google Drive indexing and information retrieval"
"Advanced OCR for complex document structures"
E-Commerce Solutions
"Shopify to CRM integration with automated email flows"
"Order processing automation with Postgres synchronization"
"Customer portal with subscription management"
Mobile Applications
"iOS and Android apps with AI chat capabilities"
"Push notification systems with deep linking"
"Enterprise SSO implementation"
Message me to discuss your project requirements, timeline, and desired outcomes for a customized proposal and implementation plan.
Steps for completing your project
After purchasing the project, send requirements so Aashiq Ali can start the project.
Delivery time starts when Aashiq Ali receives requirements from you.
Aashiq Ali works on your project following the steps below.
Revisions may occur after the delivery date.
Project Kickoff & Requirement Gathering
Review dataset/sample and goals Confirm anomaly types to detect Discuss compliance (e.g. HIPAA/GDPR) Finalize deliverables and timeline
Data Cleaning & Preprocessing
Handle missing values, duplicates, and outliers Normalize or scale features Encode categorical variables Generate exploratory data report (optional)