This Disease Prediction Application is an educational tool developed to demonstrate how machine learning models can analyze symptoms and suggest potential diseases. Designed for learning and exploration, this application allows users to select symptoms and receive a prediction based on patterns observed in the dataset used to train the model. Purpose and Functionality
The primary objective of this application is to provide insight into how machine learning can assist in symptom analysis. Using a comprehensive dataset that includes symptoms and corresponding diseases, the application leverages a Random Forest model to predict possible conditions. Users can interact with the model by selecting symptoms from a checklist and receiving a disease prediction. The application offers:
- Dataset Overview: A page displaying an overview of the dataset used for model training, including the prevalence and distribution of various symptoms and diseases.
- Data Visualization: Interactive charts showing symptom frequency, disease prevalence, correlations between symptoms, and feature importance for model predictions.
- Symptom Selection: A user-friendly checklist where users can mark symptoms they are experiencing and receive a prediction.
- Model Evaluation: A page showing model accuracy, precision, recall, and other performance metrics.
- Python: For data handling, model training, and integration.
- Scikit-learn: To build and evaluate the Random Forest model.
- Pandas and NumPy: For data manipulation.
- Streamlit: To build an interactive, web-based interface.
- Matplotlib and Seaborn: For data visualization and insights.
The application is designed to predict the following diseases based on the dataset provided. These diseases are identified using a combination of symptoms and patterns learned by the machine learning model:
1.Fungal Infection 2.Allergy 3.GERD (Gastroesophageal Reflux Disease) 4.Chronic Cholestasis 5.Drug Reaction 6.Peptic Ulcer Disease 7.AIDS (Acquired Immunodeficiency Syndrome) 8.Diabetes 9.Gastroenteritis 10.Bronchial Asthma 11.Hypertension (High Blood Pressure) 12.Migraine 13.Cervical Spondylosis 14.Paralysis (Brain Hemorrhage) 15.Jaundice 16.Malaria 17.Chickenpox 18.Dengue 19.Typhoid 20.Hepatitis A 21.Hepatitis B 22.Hepatitis C 23.Hepatitis D 24.Hepatitis E 25.Alcoholic Hepatitis 26.Tuberculosis 27.Common Cold 28.Pneumonia 29.Dimorphic Hemorrhoids (Piles) 30.Heart Attack 31.Varicose Veins 32.Hypothyroidism 33.Hyperthyroidism 34.Hypoglycemia 35.Osteoarthritis 36.Arthritis 37.Vertigo 38.Acne 39.Urinary Tract Infection (UTI) 40.Psoriasis 41.Impetigo
The diseases listed above are derived from the dataset used in the project. Predictions are based on patterns in the data and are not comprehensive or definitive for actual medical conditions.