Published Paper Details:

An extensive research project called Multiple Disease Prediction using Machine Learning, Deep Learning, and Streamlit aims to forecast multiple diseases, such as diabetes, heart disease, lung cancer, Parkinson's disease, and breast cancer. Machine learning algorithms, including support vector machines (SVM) and logistic regression, random forest, decision tree are used in this project. Streamlit Cloud and the Streamlit library are used to deploy the models, offering an intuitive interface for disease prediction. There are five disease options available on the application interface: diabetes, Parkinson's disease, breast cancer, lung cancer disease, and heart disease. The user will be asked to provide the relevant information needed for the prediction model after choosing a particular disease. The application quickly produces the disease prediction result after the requirements are provided, showing whether or not the person is impacted by the condition. This research uses machine learning techniques to address the demand for exact disease prediction, enabling early detection and prevention. People may more readily determine their risk for different diseases because of the Streamlit library's and Streamlit Cloud's simple interface, which improves accessibility and usability. The various models' high accuracy levels show how successful the machine learning algorithms used in disease prediction are.