AI – Powered PCOD Detection Platform

AI – Powered PCOD Detection Platform

Prasad Vadkar *1, Mrudula Mane*2, Madhuri Kolekar*3, Sanjana Jadhav*4
*1 Prasad Hanmant Vadkar, Computer Science , JCEP K. M. Gad, Karad ,Maharashtra,

*2Mrudula Lalaso Mane, Computer Science , JCEP K. M. Gad, Karad , Maharashtra,

*3Madhuri Kumar Kolekar, Computer Science, JCEP K. M. Gad, Karad, Maharashtra,

*4 Sanjana Arvind Jadhav, Computer Science, JCEP K.M. Gad, Karad, Maharashtra (A.N. Pawar, Computer Science, JCEP K. M. Gad, Karad, Maharashtra, India)

JAYAWANT COLLEGE OF ENGINEERING AND POLYTECHNIC , KILLEMACHINDRAGAD,SANGLI

AFFILIATED TO DR.BABASAHEB AMBEDKAR TECHNICAL UNIVERSITY, LONERE 2024-2025

Abstract

Polycystic Ovary Syndrome (PCOS), also referred to as Polycystic Ovarian Disease (PCOD), is one of the most prevalent endocrine disorders affecting women of reproductive age worldwide. It is a leading cause of anovulatory infertility and is characterized by hormonal imbalances that result in symptoms such as irregular menstrual cycles, excessive weight gain, acne, hair loss, and skin darkening. Despite its high prevalence, early-stage detection and accurate prediction of PCOS remain challenging due to limitations in existing diagnostic methods and treatment strategies.

This research aims to address these challenges by developing an advanced, computer-aided detection system utilizing machine learning (ML) and deep learning (DL) techniques. The system leverages ovary ultrasound (USG) images— one of the most reliable diagnostic modalities for PCOS—and incorporates a Convolutional Neural Network (CNN) for robust feature extraction. To enhance classification performance, a stacking ensemble model is implemented using a combination of traditional machine learning classifiers as base learners and bagging or boosting techniques as meta-learners. The CNN architecture is further strengthened through transfer learning and modern feature selection techniques such as I-SQUARE and CHI-square.

The study involves training and evaluating the proposed model on a dataset comprising 4000 ovary USG images, sourced from a publicly available PCOS dataset on Kaggle by Parson Kottarathil. Additionally, five ML classifiers— Random Forest, Support Vector Machine (SVM), Logistic Regression, Gaussian Naïve Bayes, and K-Nearest Neighbors—were evaluated on a subset of the dataset containing 41 clinical and physiological features, with the top 30 features selected for classification.

Experimental results indicate that the Random Forest Classifier outperforms other models in terms of accuracy and reliability. The proposed hybrid system significantly improves detection accuracy while reducing execution time, making it a promising solution for aiding healthcare professionals in the early diagnosis and management of PCOS.

This research lays the foundation for intelligent and scalable PCOS detection systems that integrate clinical data and medical imaging, thereby advancing personalized and timely healthcare delivery for women suffering from this condition.

Keywords:
Polycystic Ovary Syndrome (PCOS), Machine Learning, Deep Learning, Convolutional Neural Network (CNN), Medical Imaging, Ultrasound, Classification, Data Mining, Healthcare, Prediction System, Early Diagnosis