Abstract
Impulse Life Saviour is an open-source AI & Machine Learning project. Flask based web app with five machine learning models on the 10 most common disease prediction, covid19 prediction, breast cancer, chronic kidney disease and heart disease predictions with their symptoms as inputs or medical report (pdf format) as input. The Project focuses on saving life of peoples , by saving time , human errors and by early prediction of diseases or infection Through this application you can check whether you are experencing from Coronavirus or not , this can widely help the government to identify patients There are many other feature supported by this application like DISEASE PREDICTION which predicts over 10+ Main diseases based on symptoms. It is built using HTML, Machine Learning. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
The Project focuses on saving life of peoples , by saving time , human errors and by early prediction of diseases or infection Through this application you can check whether you are experencing from Coronavirus or not , this can widely help the government to identify patients There are many other feature supported by this application like DISEASE PREDICTION which predicts over 10+ Main diseases based on symptoms
Malaria Hypertension Paralysis Pneumonia Dengue Migraine Drug Reaction Dimorphic hemmorhoids(piles)
Heart Attack Cervical spondylosis Alcoholic hepatitis It also predicts CHRONIC KIDNEY DISEASE BREAST CANCER EARLY PREDICTION HEART DISEASE
2. Objective
Flask based web app with five machine learning models on the 10 most common disease prediction, covid19 prediction, breast cancer, chronic kidney disease and heart disease predictions with their symptoms as inputs or medical report (pdf format) as input.
This project demonstrates how HTML, Machine Learning can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- A modern web browser
- VS Code or any code editor
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/Elysian01/Impulse-LifeSaviour.git
cd Impulse-LifeSaviourFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Deploy the model as a web app with Streamlit, Flask or FastAPI
- Compare against an additional model and report the metric difference
- Add explainability (SHAP / Grad-CAM)
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by Elysian01 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.
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