Symptoms Based Disease Identification Using SVC

Abstract

This Flask-based application constitutes a comprehensive symptoms based disease identification platform designed to cater to a tripartite user base: patients, healthcare professionals (HCPs), and administrators. The core functionality revolves around a robust machine learning model capable of identifying potential diseases based on user-inputted symptoms. Patients can seamlessly input their symptoms, receive predicted disease diagnoses, and access accompanying information such as precautionary measures and recommended treatments. Healthcare professionals, on the other hand, are empowered to delve into patient data, refine predictive models, and offer specialized medical counsel. The administrative component ensures efficient management of HCP accounts and system oversight. Underpinning the application is a robust technological stack incorporating Flask for web development, pandas and NumPy for data manipulation, Joblib for model handling, and PyMongo for database interactions. The system leverages pre-trained models and CSV datasets for enhanced performance. Jinja2 facilitates dynamic template rendering, contributing to a user-friendly interface. This integrated platform seeks to revolutionize disease management by providing an efficient, accurate, and accessible tool for both patients and healthcare providers. The application serves as a comprehensive disease identification platform designed to augment healthcare professionals\' decision-making capabilities. Recognizing the immense pressures healthcare providers face, this platform offers a tool to enhance diagnostic accuracy and efficiency. By leveraging a sophisticated machine learning model, the system empowers clinicians to input patient symptoms and receive potential disease diagnoses, thereby expediting the diagnostic process. This rapid analysis can be particularly beneficial in time-critical situations, such as emergency departments, where swift and accurate diagnoses are paramount. Moreover, the platform provides healthcare professionals with access to relevant medical information, including treatment guidelines, medication details, and potential complications, facilitating informed decision-making. By streamlining the diagnostic process and providing essential clinical data, this application aims to mitigate the effects of stress and burnout often experienced by healthcare professionals, ultimately contributing to improved patient outcomes and a more resilient healthcare workforce. The web application also consists of a feedback mechanism which allows health care professionals to send feedback on the working of the application on disease identification further increasing scope for improving performance and accuracy of the dataset and disease identification method.

Introduction

The project "Symptoms Based Disease Identification Using SVC" is a web-based healthcare application that predicts diseases based on user-entered symptoms using a Support Vector Classifier (SVC) machine learning model. It aims to address issues in traditional healthcare such as diagnostic delays, limited access to expertise, and overburdened systems by providing an automated, accurate, and accessible tool.

The system supports three user roles—patients, healthcare professionals (HCPs), and administrators—with role-specific access and functions. Patients input symptoms through a simple interface and receive probable disease predictions, along with preventive advice, medications, diet, and workout plans. HCPs validate and enhance predictions, while administrators manage user approvals and system settings. The backend is built with Python (Flask) and MongoDB for secure data storage, with datasets stored as CSV files and a pre-trained model serialized using Joblib.

Key challenges addressed include data quality, symptom overlap, limited dataset size, input standardization, and model interpretability. The architecture involves symptom preprocessing, feature extraction, SVC-based classification, and result display, with continuous feedback improving accuracy over time.

Advantages of the system include early disease detection, cost-effectiveness, support for clinicians, scalability, non-invasiveness, and usefulness in resource-limited settings. The project employs tools such as Python, Scikit-learn, Pandas, Flask, and MongoDB to implement a robust and user-friendly platform, ultimately aiming to improve diagnostic efficiency, healthcare accessibility, and patient empowerment.

Conclusion

The developed Flask application effectively addresses the challenge of disease prediction by providing a user-friendly interface for symptom input and generating potential diagnoses based on a trained machine learning model. By integrating a search functionality and error handling mechanisms, the application enhances user experience and accuracy. The incorporation of detailed disease information, including precautions, medications, and lifestyle recommendations, empowers users to take proactive steps towards their health. The use of MongoDB Atlas as the database solution ensures efficient data storage and retrieval, supporting user authentication, HCP management, and disease data management. The application\'s modular structure and clear code organization facilitate maintainability and future enhancements. using above paragraph give me some use full lines

References

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Copyright

Copyright © 2025 Rongala Rajesh, Kuncha Lakshman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.