Inspiration
Every year, millions of people suffer and lose their lives due to preventable diseases like diabetes, heart disease, and Parkinson’s disease. According to recent studies:
- Diabetes affects over 422 million people worldwide, leading to complications such as blindness, kidney failure, and amputations.
- Heart diseases are the leading cause of death globally, accounting for approximately 17.9 million deaths annually, many of which could be prevented with early diagnosis.
- Parkinson's disease affects over 10 million people globally, causing severe mobility and coordination challenges, leading to reduced quality of life.
The inspiration behind this project is the realization that these diseases, although potentially manageable or curable, often go undiagnosed due to lack of awareness and resources. By leveraging technology, we aim to empower individuals with early detection tools to save lives.
What it does
The Multiple Disease Prediction System is an AI-powered web application that helps users predict the likelihood of three critical diseases:
- Diabetes: By analyzing health parameters like glucose levels, BMI, and insulin levels.
- Heart Disease: By assessing factors like cholesterol levels, blood pressure, and ECG data.
- Parkinson’s Disease: By evaluating voice and movement-related metrics.
The system provides instant results and personalized recommendations for consultation and preventive measures.
How we built it
- Frontend: Developed using Streamlit for an intuitive and interactive user interface.
- Backend:
- Disease prediction models built using machine learning algorithms such as Random Forest and trained on publicly available datasets.
- Models stored and loaded from the savedModels directory.
- Integration: Combined Streamlit's features with Python libraries like matplotlib and plotly for data visualization and comparison.
- Code Structure:
- Disease prediction models saved as .sav files.
- Python script (diseasePrediction.py) orchestrates the flow of inputs, predictions, and visualizations.
Challenges we ran into
- Data Quality: Identifying high-quality datasets with sufficient data points for training robust models was challenging.
- Model Accuracy: Achieving high prediction accuracy required extensive parameter tuning and model optimization.
- User Input Validation: Ensuring users provide valid and complete input data was critical for reliable predictions.
- Visualization: Creating clear and informative charts to compare user data with healthy baselines required careful design.
Accomplishments that we're proud of
- Successfully implemented three disease-specific machine learning models with high prediction accuracy.
- Developed a user-friendly interface to make medical predictions accessible to everyone.
- Integrated visualizations to compare user inputs with healthy baselines, helping users better understand their results.
- Built a scalable and modular code structure, making it easy to add more diseases in the future.
What we learned
- Machine Learning: Improved skills in training, testing, and deploying machine learning models.
- Streamlit: Leveraged its capabilities to build an interactive and visually appealing interface.
- Healthcare Insights: Gained deeper understanding of the parameters and patterns associated with diabetes, heart disease, and Parkinson's disease.
- Teamwork: Collaborated effectively to combine technical and domain-specific expertise.
What's next for HealthCare
- Expand Disease Coverage: Add predictive models for more diseases like cancer and Alzheimer’s.
- User Profiles: Allow users to save their health data and track their progress over time.
- Voice-Powered Data Entry: Simplify data input by allowing users to fill up their health profiles and answer questionnaires through voice commands.
- Doctor Integration: Enable seamless consultation with healthcare providers based on the predictions.
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