Smart Diagnosis and Diet Advice for PCOS & UTI Using Machine Learning and NLP

In order to accurately diagnose common gynecological illnesses, such as urinary tract infections (UTI) and polycystic ovarian syndrome (PCOS), this project develops the Smart Diagnosis and Diet Advice for PCOS & UTI using machine learning algorithms and NLP. For classification tasks, the current system makes use of conventional machine learning techniques including Random Forest Classifier, Decision Tree and Support Vector Classifier. By integrating improved algorithms like Gradient Boosting Models and Logistic Regression, the new method increases the efficiency and accuracy of diagnosis. Strong, automated diagnostics are made possible by the system's use of natural language processing (NLP) to assess patient history and symptoms. By reducing the time needed to discover diseases and improving diagnostic accuracy, this system provides a trustworthy medical tool.