Developed during AICTE Internship | Machine Learning-based Healthcare Solution
This project is a smart and scalable Disease Prediction System built using Machine Learning techniques as part of my AICTE internship. It aims to assist healthcare professionals and individuals in predicting the likelihood of various diseases based on clinical symptoms and medical data — enhancing early detection and improving patient care.
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Predicts multiple diseases using individual trained ML models:
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Heart Disease
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Lung Cancer
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Parkinson’s Disease
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Thyroid Disorders
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Diabetes
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Accepts real-time input of symptoms and health parameters
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Provides risk scores and predictive outcomes
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User-friendly interface with quick result generation
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Built with security and scalability in mind
| Component | Details |
|---|---|
| Language | Python |
| Libraries | scikit-learn, pandas, numpy, joblib |
| Web Framework | Streamlit / Flask |
| Models | Logistic Regression, Random Forest, SVM, etc. |
| Datasets | Public medical datasets (Kaggle, UCI, etc.) |
To develop an AI-based diagnostic assistant that helps identify diseases early by analyzing patient data through ML models — aiding in faster decision-making and personalized care.
- Integration with EHR systems
- Addition of more diseases and deep learning models
- Real-time analytics and visualizations
- Mobile-friendly deployment