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Published Paper Details:
"Predicting Polycystic Ovary Syndrome (PCOS) Using Machine Learning: A Comprehensive Approach to Data Integration, Feature Engineering, and Model Optimization"
Authors
Sumika Jain,Dr.Tarun kumar Sharma,Kuldeep chauhan
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Keywords
Polycystic Ovary Syndrome (PCOS) ,Machine Learning ,Data Integration, Feature Engineering, Model Optimization ,Random Forest, Predictive Modeling, Medical Diagnostics ,Hyper parameter Tuning ,Healthcare AI, Endocrine Disorders.
Abstract
Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder among women of reproductive age, characterized by a variety of symptoms including menstrual irregularities, hyperandrogenism, and polycystic ovaries. Accurate and early prediction of PCOS is essential for effective management and treatment. This study presents a comprehensive approach to predicting PCOS using machine learning techniques, focusing on data integration, feature engineering, and model optimization. We collected a diverse dataset comprising demographic, clinical, hormonal, and ultrasound features from a cohort of women. Rigorous data preprocessing steps, including normalization, imputation, and encoding, were employed to ensure data quality and consistency. Advanced feature engineering techniques were applied to enhance the predictive power of the input variables, followed by the selection of the most relevant features through correlation analysis and feature importance metrics. Various machine learning models, including Logistic Regression, Random Forest, Support Vector Machines, and Neural Networks, were trained and evaluated. Hyper parameter tuning was conducted using Grid Search and Cross-Validation to optimize model performance. The models were assessed based on accuracy, precision, recall, F1 score, and ROC-AUC metrics. Our results demonstrate that the optimized Random Forest model achieved the highest predictive accuracy and robustness, with a notable improvement in identifying PCOS cases compared to traditional diagnostic methods. The study highlights the potential of machine learning in enhancing PCOS diagnosis, offering a reliable, non-invasive, and scalable solution for clinical application. Future work will focus on integrating genetic data and expanding the dataset to further improve model generalizability and accuracy. This research underscores the importance of leveraging machine learning in medical diagnostics and provides a foundation for developing advanced predictive tools for PCOS and other complex disorders.
IJCRT's Publication Details
Unique Identification Number - IJCRT24A5401
Paper ID - 262226
Author type - Indian Author
Page Number(s) - m332-m337
Pubished in - Volume 12 | Issue 5 | May 2024
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No Of Downloads - 7
Author Country - India, 247341, Saharanpur, Saharanpur, 247341, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Sumika Jain,Dr.Tarun kumar Sharma,Kuldeep chauhan, ""Predicting Polycystic Ovary Syndrome (PCOS) Using Machine Learning: A Comprehensive Approach to Data Integration, Feature Engineering, and Model Optimization"", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 5, pp.m332-m337, May 2024, Available at :http://www.ijcrt.org/papers/IJCRT24A5401.pdf
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