Multiple Disease Prediction Webapp

Abstract : The designed web app employs the Streamlit Python library for frontend design and communicates with backend ML models to predict the probability of diseases. Multiple Disease Prediction has many machine learning models used in prediction. We will be able to choose the diseases from the navigation bar or a sidebar for which we want to make a prediction using various input values. These input values will be the symptoms, physical health data, or blood test results. We will first trained our model from historic data, so it can make accurate predictions. It's capable of predicting whether someone has Diabetes, Heart issues, Parkinson's, Liver conditions, Hepatitis, Jaundice, and more based on the provided symptoms, medical history, and results.

Project Members

1. KHAN MOHAMMED DANISH NISAR AHMED [ Team Leader ]
2. MANJREKAR SOHAM SHRIKANT
3. KHAN MUZAFFAR MOHAMMAD SOAIB
4. JAMADAR DANISH RASHID

Deployment Steps

Please follow the below steps to run this project.

1. pip install -r requirements.txt
   
2. cd frontend
   
3. streamlit run multiple_disease_prediction.py
   
   

Platform, Libraries and Frameworks used

1. Streamlit
2. Python
3. Sklearn

Dataset Used

1. Diabetes disease dataset
2. Heart disease dataset
3. Parkinsons disease dataset
4. Liver disease dataset
5. Hepatities disease dataset
6. Jaundice disease dataset

Trailer

How-to-Build-Multiple-Disease-Prediction-Website.mp4

Research Paper: Multiple Disease Prediction Webapp

• Description: A study that developed a web application capable of predicting multiple diseases, including diabetes, heart disease, Parkinson's, liver disease, jaundice, and hepatitis, using machine learning algorithms such as SVM, Decision Tree, and Random Forest. The system allows users to input data for a specific disease, and based on the trained model, the output is displayed. The paper also discusses the functional and non-functional requirements of the system, as well as the architecture design and implementation details.
• Online JETIR Paper
• Letter of Acceptance
• Certificate of Each Member

References

• [1] Priyanka Sonar, Prof. K. Jaya Malini,” DIABETES PREDICTION USING DIFFERENT MACHINE LEARNING APPROACHES”, 2019 IEEE ,3rd International Conference on Computing Methodologies and Communication (ICCMC)
• [2] Archana Singh, Rakesh Kumar, “Heart Disease Prediction Using Machine Learning Algorithms”, 2020 IEEE, International Conference on Electrical and Electronics Engineering (ICE3)
• [3] A. Sivasangari, Baddigam Jaya Krishna Reddy, Annamareddy Kiran, P. Ajitha,” Diagnosis of Liver Disease using Machine Learning Models” 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)