Creation and deployment of an application that predicts whether a set of inputs correspond or not to a disease.
- About
- Project Procedure
- List of Diseases
- Dataset Information
- Project Deployment
- Resources
- Languages Used
- Other Resources
- Other Information
- Notes
The initial project consisted of a set of models created using ML and DL Models based on different disease datasets acquired from different sources.
The initial project had to be separated into two separate projects. A project that takes numerical inputs and another project that takes images as inputs to predict whether an image corresponds to a disease or not.
This part of the project consists of models that require numerical inputs.
Creation and deployment of an application that predicts whether a set of inputs correspond or not to a disease.
- [Back to Table of Contents](#table-of-contents
Dataset:
Dataset Acquisition.
Dataset Analysis.
Model:
Model Creation.
Model Testing.
Model Deployment.
Application:
Application Creation.
Application Testing (Local and External).
Application Modification (Local and External).
Application Deployment (Local and External).
Documentation:
Creation of Required Documentation.
Cancer.
Diabetes.
Heart Disease.
Liver Disease.
Kidney Disease.
* [Back to Table of Contents](#table-of-contents)
The information for the acquisition of each disease was gathered by different entities and with different methods.
Each Dataset contains specific information and whether a user has or not the disease.
The sources used for the acquisition of the datasets are the following
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Taken from: UCI.
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For more information about cancer please visit: cancer.net).
- Sources:
- Dua, D. and Graff, C. (2019). Irvine, CA: University of California, School of Information and Computer Science. UCI Machine Learning Repository.
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Taken from: NIDDK.
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Dataset also located on: Kaggle.
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For more information about diabetes please visit the following websites:
- Sources:
- Donor of database: Vincent Sigillito ([email protected]) Research Center, RMI Group Leader Applied Physics Laboratory The Johns Hopkins University Johns Hopkins Road Laurel, MD 20707 (301) 953-6231.
- (NIDDK) National Institute of Diabetes and Digestive and Kidney Diseases
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Taken from: UCI.
-
For more information about heart disease please visit the following websites:
- Sources:
- Dua, D. and Graff, C. (2019). Irvine, CA: University of California, School of Information and Computer Science. UCI Machine Learning Repository.
- Sources:
- Dua, D. and Graff, C. (2019). Irvine, CA: University of California, School of Information and Computer Science. UCI Machine Learning Repository.
-
Taken from: UCI.
-
For more information about liver disease please visit the following websites:
- Sources:
- Dua, D. and Graff, C. (2019). Irvine, CA: University of California, School of Information and Computer Science. UCI Machine Learning Repository.
Different languages and resources where used in order for this application to be deployed.
Data Analysis.
Model Creation.
Model Deployment.
Website - Front-End for model deployment.
Medical Web App based on:
The model creation decision was done taking in consideration various factors, some of them involving the app deployment on the web.
- Based on Shobhit Srivastava's and Karan Mehra's Project
- For further questions about the way this project was made or to see other projects made by me please click here
- For further questions about the way this project was made or for further inquiries e click here
This project was not endorsed by any company.
Special gratitude to all the people who make the datasets and all the information available.
The application and information displayed here is for educational purposes. The accuracy of the models varies and will make some prediction errors. This should not be taken as medical advice.