Abstract:
Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting women
of reproductive age, necessitating accurate prediction and diagnosis for effective patient
care and personalized treatment. Despite numerous studies on PCOS, there remains a
gap in the literature regarding the most effective classification algorithms and feature
selection methods tailored to PCOS datasets, as well as a comparative analysis of data
science tools like Python and RapidMiner in this context. This study aims to address
these gaps by identifying the most effective machine learning classification methods
for predicting PCOS, determining the significant features involved, and comparing the
performance of Python and RapidMiner tools. Key steps include data preprocessing,
constructing and evaluating a stacking classifier using Python, and implementing
various K-fold cross-validation settings to assess training and test accuracies, precision,
and recall. The research examines variables of infertility and non-infertility within the
dataset, using a Stacking Classifier model with 37 features to report on cross-validation
results. Findings contribute to advancing PCOS prediction by leveraging machine
learning techniques and provide insights into the efficiency of Python and RapidMiner
in healthcare data science. These outcomes have implications for customizing
healthcare, enhancing patient care, and optimizing operational efficiency, ultimately
aiming to improve PCOS management and treatment through advanced data science
methodologies.
