Automatic descriptive answer evaluator using machine learning.

Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Early diagnosis and effective treatment are crucial for managing the symptoms and improving the quality of life for patients. This project aims to develop an intelligent system for the diagnosis and treatment suggestion of Parkinson's disease using machine learning (ML) models and natural language processing (NLP) techniques. The proposed system leverages a Random Forest Classifier for the accurate diagnosis of Parkinson's disease based on clinical data and patient symptoms. The Random Forest algorithm, known for its robustness and high accuracy, is trained on a comprehensive dataset to identify patterns and predict the presence of Parkinson's disease with high precision. For treatment suggestions, the system incorporates a chatbot powered by the Natural Language Toolkit (NLTK). The chatbot interacts with patients, gathers information about their symptoms, and provides personalized treatment recommendations based on established medical guidelines and expert knowledge. The NLTK-based chatbot ensures a user-friendly and interactive experience, making it easier for patients to receive timely and relevant treatment advice. The integration of machine learning and natural language processing in this system offers a promising approach to enhance the diagnosis and management of Parkinson's disease. By providing accurate diagnostic results and personalized treatment suggestions, the system aims to support healthcare professionals and improve patient outcomes.