Symptom Checker Framework: Leveraging Machine Learning for Early Diagnosis in Healthcare Systems

The early and accurate identification of diseases based on symptoms is a critical factor in effective healthcare. In this project, we introduce DignoSmart: Your Personalized Symptom Check, an intelligent, machine-learning-based application designed to assist users in identifying potential health conditions by entering their symptoms. The system utilizes a decision tree algorithm to predict possible diseases based on user input through a chat-like interface. Users begin by providing personal information, such as age and symptoms. The application processes this data and, through a series of follow-up questions, refines its understanding of the user's health condition. These questions, related to symptom duration, pain intensity, and specific issues like back pain, guide the prediction process. The decision tree algorithm, trained on a dataset of symptoms and disease correlations, predicts the most likely condition based on the responses. The system then provides a detailed description of the predicted disease, including key information about the condition and possible causes. Additionally, it offers recommendations for basic precautions and suggests when to seek medical attention. By integrating machine learning techniques and an intuitive user interface, DignoSmart aims to assist users in recognizing potential health problems early and taking appropriate action, thus contributing to better health outcomes..