- Download 17
- File Size 4.35 MB
- File Count 1
- Create Date 07/06/2025
- Last Updated 09/06/2025
Multi Disease Prediction Using Machine Learning
Multi Disease Prediction Using Machine Learning
Buddhi Akhil, ECE ,Institute of Aeronautical Engineering, Hyderabad, India
22951A0412@iare.ac.in
Dr. S China Venkateshwarlu,Professor of ECE ,Institute of Aeronautical Engineering, Hyderabad, India c.venkateshwarlu@iare.ac.in
Dr. V Siva Nagaraju,Professor of ECE ,Institute of Aeronautical Engineering, Hyderabad, India v.sivanagaraju@iare.ac.in
Mr.U.Somanaidu 4,Asst. Professor of ECE ,Institute of Aeronautical Engineering, Hyderabad, India
u.somanaidu@iare.ac.in
Abstract:
Multiple Disease Prediction using Machine Learning, Deep Learning, and Streamlit is a comprehensive project aimed at predicting diseases such as diabetes, heart disease, and Parkinson’s disease. The system leverages a combination of machine learning and deep learning algorithms, including TensorFlow with Keras, Support Vector Machine (SVM), and Logistic Regression, to build accurate and reliable prediction models. These models are trained on publicly available datasets and are deployed using Streamlit Cloud, utilizing the Streamlit library to provide an intuitive and user-friendly interface.
The application allows users to choose from the supported disease categories—heart disease, diabetes, and Parkinson’s disease. Upon selecting a disease, users are prompted to enter specific medical parameters relevant to that condition. After submitting the required information, the system processes the inputs through the trained model and delivers a real-time prediction, indicating the likelihood of the disease being present.
To ensure robust performance, the models undergo preprocessing steps including feature encoding and scaling, improving the training effectiveness and generalizability of the system. Model evaluation is performed using standard accuracy metrics, and the results show promising predictive capabilities for each condition.
This project addresses the growing need for accessible, technology-driven health assessment tools. By combining advanced machine learning techniques with an easy-to-use interface, it empowers users to monitor their health status and take preventive measures. Future enhancements could include integration with real-time data from wearable devices, increasing prediction accuracy and enabling continuous health monitoring.
