Natural Language Processing (NLP) in Disease Detection — A Discussion of How NLP Techniques Can Be Used to Analyse and Classify Medical Text Data for Disease Diagnosis

The project "Natural Language Processing (NLP) in Disease Detection" explores the application of machine learning and NLP techniques to analyze and classify medical text data for disease prediction. The primary aim is to develop a system that can predict potential diseases based on the symptoms entered by users, thus aiding in early diagnosis. The dataset used in this project contains textual descriptions of various symptoms along with their corresponding disease labels. These texts serve as the independent variable, while the disease labels are the target for classification. To achieve this, multiple machine learning algorithms, including Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and Recurrent Neural Networks (RNN), were employed to classify the diseases based on symptoms. Among these, RNN emerged as the most effective model, particularly in handling sequential data and accurately predicting diseases from textual inputs.

The system offers an intuitive web interface built using HTML, CSS, and JavaScript for the frontend, while the backend is powered by Python and the Flask framework. Users can register, log in, and input their symptoms into the system, which then processes the text and predicts the disease. This automated approach aims to simplify the process of disease diagnosis, making it more accessible and efficient for individuals with limited access to healthcare professionals. By combining NLP and machine learning, this project demonstrates the potential of technology to aid in healthcare-related decision-making, providing a reliable tool for users to gain insights into their symptoms and potential medical conditions.

Keywords: Natural Language Processing, Disease Detection, Machine Learning, Classification, Logistic Regression, Naive Bayes, KNN, ANN, RNN, Symptom Classification