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, data-driven tool that minimizes errors, reduces diagnostic time, and improves patient care.

Introduction

The project “Gynecology Disease Diagnosis Expert System Based on Machine Learning and Natural Language Processing” aims to develop a smart, user-friendly system to assist in early and accurate diagnosis of common gynecological disorders—specifically Urinary Tract Infection (UTI) and Polycystic Ovary Syndrome (PCOS). It combines Machine Learning (ML) to analyze medical data with Natural Language Processing (NLP) to interpret patient inputs in conversational language.

The system improves diagnostic accuracy by comparing multiple ML models (Logistic Regression, Decision Tree, Random Forest, SVM, etc.), with Random Forest achieving the highest accuracy (~91.3%). It processes both structured clinical data and unstructured text inputs by extracting relevant features using NLP techniques like tokenization and Named Entity Recognition.

Implemented as a web-based interface (using Streamlit), users can input symptoms naturally, receive diagnostic predictions with confidence scores, and access medical guidance. The system was tested and validated with cross-validation and expert feedback to ensure reliability.

Overall, the project seeks to enhance accessibility, reduce diagnostic time, minimize errors, and support healthcare professionals in providing personalized, data-driven gynecological care.

Conclusion

The development of the Gynecological Disease Diagnosis Expert System (GDDES) marks a significant advancement in the field of automated healthcare diagnostics, particularly for gynecological disorders such as Urinary Tract Infection (UTI) and Polycystic Ovary Syndrome (PCOS). By leveraging the power of machine learning algorithms and natural language processing, GDDES provides a highly accurate and efficient tool for diagnosing these conditions. The integration of advanced algorithms like Logistic Regression and Gradient Boosting Models, alongside traditional methods such as Decision Trees, Random Forest, and Support Vector Classifier (SVC), ensures a comprehensive and robust diagnostic process. Moreover, the application of NLP in analyzing patient records and symptoms enhances the system\'s ability to interpret and process complex medical data, thereby improving the overall diagnostic precision. This system not only reduces the time required for disease identification but also aids healthcare professionals in making more informed decisions, ultimately leading to better patient outcomes. The GDDES represents a significant step forward in the use of technology to support medical diagnostics, offering a reliable and scalable solution that can be adapted to a variety of clinical settings.

References

[1] Patel, S., Jalali, M. S., & Mehta, K. G. (2023). Machine LearningBased Diagnostic Models for Polycystic Ovary Syndrome (PCOS) and Their Implications in Clinical Practice. Journal of Women\'s Health, 32(3), 325334. [2] Wang, Y., Sun, X., & Li, H. (2023). Applications of Machine Learning in Urinary Tract Infection Diagnosis: A Review. Computational and Structural Biotechnology Journal, 21(2), 456465. [3] Zhu, X., Zhang, L., & Liu, Y. (2022). Natural Language Processing in Electronic Health Records for Gynecological Disease Diagnosis. Journal of Biomedical Informatics, 132(2), 104021. [4] Smith, P., & Anderson, R. (2022). Evaluating the Effectiveness of Decision Tree and Random Forest Algorithms in Predicting Gynecological Diseases. International Journal of Medical Informatics, 160(2), 104627. [5] Gupta, N., & Reddy, B. K. (2023). A Comprehensive Review on Deep Learning Approaches for Gynecological Disease Classification. IEEE Access, 11, 1234512358. [6] Mitra, S., & Basu, A. (2023). Role of Logistic Regression and Gradient Boosting in Enhancing Gynecological Disease Diagnosis. Journal of Medical Systems, 47(1), 101. [7] Chen, H., & Wang, T. (2022). Application of Convolutional Neural Networks in the Detection of Gynecological Diseases. IEEE Transactions on Medical Imaging, 41(5), 13971406. [8] Bhardwaj, M., & Khurana, S. (2023). Automated Diagnosis of Gynecological Disorders Using Machine Learning Techniques. Journal of Artificial Intelligence in Medicine, 132(2), 102129.

Copyright

Copyright © 2025 Nukalapati Vinod, Thallaplli Maheswari, Pulipati Pavithra, Tiruveedula Meghana, Ms. Guna Gayathri Praseetha K M.E. 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.