Symptom Based Disease Prediction System

A web-based Symptom Based Disease Prediction System that uses machine learning approaches to help with early disease diagnosis is presented in this research.Through an intuitive interface, users can enter their symptoms, and the system uses a trained Random Forest Classifier to determine the most likely condition.This model can provide precise predictions even for intricate symptom combinations because it was trained on large dataset of common symptoms and related illnesses.The system raises awareness and directs users toward the right course of action by predicting diseases and offering suggested drugs and preventative measures.The application, which was created with Python and the Flask web framework, incorporates crucial machine learning tools like Scikit-learn, making model training, prediction, and evaluation easier.To guarantee accurate disease predictions, the system assesses several supervised learning algorithms and chooses the model with the best accuracy.The application includes session management, registration forms, and safe user and admin login features.Frequent users can export or delete previous record, follow their forecast history, view their prediction result, and choose symptoms.Administrators have access to an extensive dashboard that offers information on how the system is being used, including the number of users, the number of forecasts, the most common diseases predicted, and recent prediction logs.This method helps patients and medical professionals by automating the first disease assessment process, which lessens the need for manual evaluation and may reduce the rate of misdiagnosis.Because of its scalable design, it can be further integrated as a decision support system into clinics, hospitals, or telemedicine systems.All things considered, this study shows how machine learning can enhance healthcare accessibility, offer prompt disease insights, and function as a trustworthy initial diagnosis tool.