Year: 2025
Type: article
Abstract: Polycystic Ovarian Disease (PCOD) and Polycystic Ovarian Syndrome(PCOS) are prevalent hormonal disorders that impact women's reproductive health, metabolic functions, and overall well-being. Given the complexities associated with these conditions, early detection is crucial to prevent long-term complications such as infertility, diabetes, and cardiovascular diseases. This project aims to develop a machine learning-driven system for the early detection, prediction, and classification of PCOD/PCOS. The system integrates multiple medical data inputs, including blood test results and hormonal profiles, to accurately diagnose the condition. Machine learning algorithms analyze these inputs to identify patterns indicative of PCOD/PCOS, offering a reliable diagnosis at an earlier stage. The system also predicts the likelihood of the disease's progression based on key indicators such as hormone levels and metabolic markers. Furthermore, the condition is classified into various stages based on severity—ranging from mild to severe—taking into account symptom intensity and hormonal imbalances. Once the stage is determined, the system generates customized diet and exercise plans tailored to the individual's health status and condition stage. These personalized management strategies aim to regulate hormonal imbalances, promote weight management, and mitigate the symptoms of PCOD/PCOS. By integrating early detection, prediction, and personalized intervention plans, the system offers a comprehensive approach to managing the disorder, ultimately improving patient outcomes and quality of life.
Authors: Thommandra Rishika, Veeramalla Vishnu Vardhan, Nadipelly Sai Kiran, Mr. Baradur Kumar, Dr. M. Ramesh
Institution: Jawaharlal Nehru Technological University, Hyderabad
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410748065