Advancements in Disease Diagnosis and Prediction Leveraging Insights Extraction and Computational Learning Techniques

Technology has revolutionized the health domain largely. Accurate and on-time analysis of any health-related problem It is crucial for the prevention and treatment of the ailment. The traditional way of diagnosis may in the event of a severe illness, it may not prove to be satisfactory. This project aims to design a diagnostic framework for different diseases based on their symptoms. This system utilizes data mining methodologies including classification techniquesin order to achieve such a mode and also ML algorithms for prediction of any diseases can help in a more accurate diagnosis than the conventional method. We have introduced a system for disease prediction that employs several machine learning algorithms. The dataset contains information on symptoms, age, blood group, and gender of individuals, which is used for processing by the diagnosis system. The output of the system is the disease. Here, the SympoDx algorithm, which incorporates Random Forest, Decision Tree, KNN, and Support Vector Classifier, is employed for classification tasks and computes the probabilities of diseases. Following the results, patients can contact the doctor accordingly for further treatment.