Detection Techniques of PCOS Using Supervised Machine Learning and CNN
DOI:
https://doi.org/10.21467/proceedings.7.6.25Keywords:
Polycystic Ovary Syndrome, Machine Learning, Early DiagnosisAbstract
The endocrine disorder known as Polycystic Ovary Syndrome affects the majority of young female patients. Most patients experience infertility together with various long-lasting health problems because of this condition. The main objective is to detect PCOS early so a personalized care strategy can be implemented for its effective management. The authors research machine learning methods which identify PCOS through hormonal assessments and ultrasound imaging and patient medical histories. Three machine learning algorithms including decision trees, support vector machines and neural networks assess PCOS predictability in this investigation. The completion of this project faced challenges in data preprocessing and feature selection along with model optimization problems. The testing outcomes point to the convenience of developing such a device for PCOS early medical detection alongside patient-specific therapy.
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