You will get Diphtheria Outbreak Prediction System
Project details
You will get a high-precision AI system that predicts diphtheria outbreaks with 90%+ accuracy, enabling early intervention and saving lives. With my background in both engineering and medical AI, I deliver robust, scientifically-validated models that integrate seamlessly into healthcare workflows. I've successfully built predictive systems analyzing 3,500+ clinical records, achieving 92% accuracy in outbreak forecasting. My approach combines rigorous data science with practical healthcare applications, ensuring your system is both technically sound and medically useful.
Machine Learning Tools
Google Data Studio, Microsoft Excel, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$1,000
|
Standard
$3,500
|
Advanced
$6,000
|
|---|---|---|---|
| Delivery Time | 15 days | 20 days | 30 days |
Number of Revisions | 2 | 4 | 6 |
Number of Graphs/Charts | 7 | 10 | 15 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$250 - $500
Additional Graph/Chart
(+ 3 Days)
+$50Frequently asked questions
About Femi
Full-Stack Developer | Academic Researcher | Python/React/Django
Lagos, Nigeria - 8:38 am local time
Software Development Expertise:
I specialize in building scalable web applications using Python, Django, and Django REST Framework for backend development, paired with modern frontend technologies like React, Next.js, and JavaScript/TypeScript. I have extensive experience designing and integrating REST APIs, working with PostgreSQL databases, and implementing third-party API integrations including Stripe, payment gateways, and various SaaS platforms.
My infrastructure skills include Docker containerization, AWS cloud deployment, and CI/CD pipeline setup using GitHub Actions or similar tools. I am comfortable with Redis caching, Celery for background task processing, and building microservices architectures for scalability.
I have successfully delivered multi-tenant SaaS platforms, ERP systems, school management systems, fintech applications, and AI-powered products. I understand the full development lifecycle from database schema design and API architecture to deployment and ongoing maintenance.
Academic Research & Writing Expertise:
I also bring strong academic research and writing capabilities. I have worked on numerous research projects involving literature reviews, systematic reviews, meta-analysis, data analysis using SPSS and Python, and thesis/dissertation editing across multiple disciplines including healthcare, environmental science, machine learning, and social sciences.
I am proficient in quantitative and qualitative research methods, statistical analysis, and academic formatting including APA, IEEE, and Harvard styles. I have experience preparing manuscripts for Scopus-indexed journals, conference papers, and PhD theses.
I can also assist with AI training and evaluation projects, technical report writing, and content creation for academic and professional audiences.
What I Offer:
Full-stack development (Python/Django/React)
REST API design and third-party integrations
Database design and optimization (PostgreSQL)
Cloud deployment (AWS, Docker, CI/CD)
Academic writing, editing, and proofreading
Research methodology and data analysis
Literature reviews and systematic reviews
Thesis and dissertation support
AI/LLM integration and evaluation
I am reliable, detail-oriented, and committed to delivering high-quality work on time. I communicate clearly and work independently while collaborating effectively with teams.
Let's build something great together, whether it is a scalable SaaS platform or a publishable research paper.
Steps for completing your project
After purchasing the project, send requirements so Femi can start the project.
Delivery time starts when Femi receives requirements from you.
Femi works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1: Medical Data Analysis & Requirements
Analyze available clinical datasets (3,500+ records) Consult with medical experts on prediction parameters Define prediction accuracy targets (90%+) Document data privacy and ethical considerations
Data Engineering & Preprocessing
Clean and normalize medical dataset Handle missing values and outliers Perform feature engineering for predictive power Split data into training, validation, and test sets Create reproducible data processing pipeline