Symptom Based Disease Prediction

Predictive disease is an important part of preventive medicine because it reports possible diseases in their immature form just before their symptoms appear. Clinical expertise is frequently involved in the process of manual diagnosis, and is not always available or accurate in a time sensitive scenario. The project, Disease Prediction from Symptoms, is an initiative that uses the practices of Machine Learning to help with determining likely diseases based on the input of symptoms provided by a user to the system. The system is developed in Python, and it has a Tkinter-based GUI to allow a secure login and registration, and an interactive dashboard that allows one to add symptoms. The input is processed by a trained classification model which is trained by applying algorithms like the Random Forest and Decision Tree among others and the most likely disease is estimated. The model has been trained based on a structured data set of wide spectrum of diseases and combination of symptoms. The application does not only prognose the disease but also offers precautionary options and some general medical care tips, which are helpful in early diagnosis and intervention prior to a visit to a health care specialist. This project adds to the accessible, affordable, and reliable health help by offering the combination of information-based learning and user-friendly interaction. The implementation can be improved in future by incorporating electronic health records, real-time monitoring of patient status, and deep learning models to achieve better accuracy.