Multiple Disease Prediction Web App using Machine Learning

Multiple Disease Prediction Web App using Machine Learning Algorithm Steps Involed for Prediction: Step 1: I developed a disease app that uses machine learning to predict and diagnose, but it's definitely feasible with the right resources and expertise.

Step 2: Collect and clean data: The first step in building a machine learning app for disease prediction is to gather a large dataset of patient information, including symptoms, medical history, lab test results, and any other relevant information. This data should be cleaned and preprocessed to ensure that it's in a usable format for machine learning algorithms.

Step 3: Choose an appropriate algorithm: Next, you'll need to select a machine learning algorithm that is well-suited to the task of disease prediction. I used SVM(Support Vector Machine) for Diabetes prediction and Parkinson prediction, Logisitc Regression for Heart Disease prediction.

Step 4: Train the model: Once you've chosen an algorithm, you'll need to train the machine learning model using your dataset. This involves feeding the algorithm with input data and output (labelled) data, allowing it to learn patterns and correlations between different features and diseases.

Step 5: Validate and fine-tune the model: After training, you'll need to validate the model by testing it on a separate dataset of patient information that wasn't used during the training phase. This will help you to evaluate the model's accuracy and identify any potential issues. If necessary, you may need to fine-tune the model to improve its performance.

Step 6: Build the app: Once you've trained and validated your machine learning model, you can integrate it into a mobile app or web app that allows users to input their symptoms and receive predictions for different diseases. The app should also include a user interface that is easy to navigate and understand.

For Deployment this Machine Learning model i used Streamlit on Heroku