Enhancing Disease Diagnosis with Machine Learning Based Symtom Prediction

Effective symptom-based disease prediction is essential for prompt patient care and management. Investigating the use of machine learning and data mining in the analysis of the rapidly expanding medical data. The new method makes use of decision tree for trait-based subgrouping, Random Forest for increased precision, support vector machine for managing complicated datasets, K-nearest neighbor for similarity-based predictions, and logistic regression for binary categorization. These techniques provide important insights for precise forecasting by revealing hidden patterns in vast amounts of disease-related data. The dataset: symptom and severity shows promise for preventative healthcare by effectively predicting diseases by taking into account a variety of symptoms.