PCOS And UTI Diagnosis Expert System Using Machine Learning Algorithm and NLP Technique

DOI

10.2991/978-94-6463-858-5_30How to use a DOI?

Keywords

Preliminary Diagnosis of PCOS and UTI; Supervised learning; Decision trees; Logistic models; Gradient boosting

Abstract

The objective of this project, the system is improved through the application of machine learning as well as natural language processing to diagnose the common diseases predominantly affecting females that include Urinary Tract Infection (UTI) as well as Polycystic Ovary Syndrome (PCOS). The constructiveness of the current structure employs traditional probabilistic schemes including the Decision Tree, Random Forest Classifier, SVC, Naïve Bayes, and the K-Nearest Neighbor to classify. The performance and diagnostic capability increases with the help of algorithms such as Gradient Boosting Models in the proposed system. As such, it leans on the power of the NLP algorithm to scour through the patient records and symptoms for a completely automated diagnosis. The above approach is meant to enhance precision in detection or diagnosis processes as well as the amount of time needed for such diagnosis so as to develop a tool that can be regarded as reliable in the profession.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - V. S. S. P. Raju Gottumukkala
AU  - Bendapudi Gnana Vyshnavi
AU  - Bandaru Sai Sri Navya
AU  - Geddam Ishwarya
AU  - Chennuboyina Rama Swathi
AU  - M. Narasimha Raju
PY  - 2025
DA  - 2025/11/04
TI  - PCOS And UTI Diagnosis Expert System Using Machine Learning Algorithm and NLP Technique
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 343
EP  - 353
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_30
DO  - 10.2991/978-94-6463-858-5_30
ID  - Gottumukkala2025
ER  -