Paper Contents
Abstract
This script implements an intelligent interactive system for disease diagnosis and drug recommendation. The application uses machine learning techniques such as Decision Trees and Support Vector Machines to classify the diseases based on user-reported symptoms. It is aimed to provide a preliminary diagnosis and recommend medications, making health care more accessible and convenient. Users interact with the system through a graphical interface that is developed using Python's Tkinter library.Users may enter the symptoms, which are checked and matched against a database of pre-defined symptoms that is stored using pattern recognition techniques. According to this input, the program suggests the possible diseases by its decision tree model, which was trained and also evaluates the degree of symptoms.The system integrates a comprehensive dataset comprising symptoms, severity levels, and precautionary measures. It calculates the probability of disease severity to check whether medical consultation is needed. Moreover, it offers personalized advice, such as precautionary steps, based on the predicted condition. An SQL database is used to retrieve drug recommendations for diagnosed diseases. The program includes interactive features, such as live updates, text-to-speech functionality, and graphical feedback, enhancing user engagement and understanding. Experimental results demonstrate the accuracy and usability of the model, thereby making it a valuable tool in preliminary healthcare applications.
Copyright
Copyright © 2025 Rakshith S. This is an open access article distributed under the Creative Commons Attribution License.
