Gynaecological Disease Diagnosis Expert System Based on Machine Learning Algorithm and Natural Language Processing

Abstract: The primary objective of the Gynaecological Disease Diagnosis Expert System (GDDES) project is to develop an advanced diagnostic tool that leverages machine learning algorithms and Natural Language Processing (NLP) to accurately identify and diagnose common gynecological disorders, specifically Urinary Tract Infection (UTI) and Polycystic Ovary Syndrome (PCOS). The project aims to improve diagnostic accuracy by implementing and comparing the performance of traditional machine learning models such as Decision Tree, Random Forest, Support Vector Classifier, Naïve Bayes, CNN LSTM and K-Nearest Neighbor with advanced algorithms like Logistic Regression and Gradient Boosting Models. Additionally, it seeks to enhance the system's capability to analyze and interpret unstructured patient data through NLP, thus facilitating a more efficient and automated diagnostic process. Ultimately, the goal is to provide healthcare professionals with a reliable, datadriven tool that minimizes errors, reduces diagnostic time, and improves patient care.