Challenge background
The increase of infectious diseases in Sudan, coupled with the complex environmental and socio-economic factors, hold significant challenges to the existing healthsystem. Traditional disease surveillance methods often rely on retrospective data analysis, which may be insufficient for responding to emerging outbreaks. A robust forecasting system can provide early warnings, enabling timely interventions and resource allocation.
This project aims to develop a disease forecasting model using machine learning techniques and deploy it as a user-friendly web application. By leveraging historical data, real-time information, and ML algorithms, this system will enable accurate prediction of disease impact, empowering healthcare professionals and officials to make informed decisions.
The problem
The conflict areas, displacement and resource constraints have severely impacted healthcare access. Many facilities are under-resourced and understaffed, leaving critical gaps in addressing medical conditions. Alongside patients of chronic conditions such as diabetes and hypertension, the region faces threats of frequent outbreaks of infectious diseases like malaria, cholera, poisoning and pneumonia. Each area and topic requires tailored solutions in order to address such health challenges. Disease management and prediction using ML techniques work as aiding tools for medical professionals and provide a machine-aided solution.
Goal of the project
This project aims to develop a robust and scalable solution through:
- Data Collection: Literature Review, Collecting historical data, incorporating real-time data (weather, news), and gathering demographic and population data.
- Data Preprocessing: Removing inconsistencies, standardizing units and including seasonal and geographic trends.
- EDA: Understanding disease patterns and correlations with social/environmental factors. Visualizing gaps in healthcare access.
- Modeling: Developing a hybrid model, using clusters as labels for supervised tasks. Developing a predictive model using the labeled data.
- Model Deployment: Creating a web-app, dashboard or API, to provide real-time insights.
- Simple Design: Building a user-friendly interface to present insights and recommendations in an accessible way. The solution will enable communities and healthcare providers to address medical emergencies and optimize resources, as well as inspire similar projects.
Project timeline
- 1
Week 1
Data Collection & Literature review: Collect and clean the required historical and healthcare data (disease prevalence, healthcare access, environmental data, etc.). Data wrangling and merging health data with geospatial data(regions, facilities).
- 2
Week 2
Data Pre-processing and EDA: Perform EDA to understand distributions and trends. Prepare features for both unsupervised (clustering) and supervised (disease prediction) models. Analyze/Identify high-risk or underserved regions
- 3
Week 3
Clustering and Data Labeling: Apply clustering algorithms to create labeled data. Fine-tune clustering models and explore how the clusters can be interpreted for healthcare intervention. Integrate clustering output as features for the supervised model.
- 4
Week 4
Modeling (Supervised learning): Train a predictive model to predict disease outbreak or prevalence based on historical data and features derived from clustering. Evaluate the model with cross-validation and performance metrics.
- 5
Week 5
Deployment: Build an interactive dashboards and deploy the API on a cloud platform for users to interact with. Prioritize deploying a simple functional version of the model with clear actionable insights.
- 6
Week 6
API Front-end & Testing: Deploy an interactive interface, include features such as input for real-time data (e.g., weather updates, population movements) and output for predictions.
What you'll learn
By working on this project you will gain practical experience:
- Working on real-time problems to design tools that serve communities and Public Health.
- Sourcing data from literature, and collecting datasets.
- Ensuring data quality, privacy and learning about ethical practices in Health-AI.
- Predictive analysis and using unsupervised and supervised Learning algorithms.
- Building APIs, hosting the visual platform and designing the interface.