Abstract:
Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality &
one of the primary causes of anovulatory infertility in women globally. The real-world
clinical PCOS detection technique is critical since the accuracy of interpretations being substantially dependent on vast numbers of symptoms & physician’s expertise. An artificially
intelligent PCOS detection system might be a feasible alternative to the typical diagnostic
procedure. Thus, the objectives of this study are: to propose intelligent computer-aided
PCOS detection techniques based on patient symptom data & ovary ultrasonography(USG)
images; as well as to compare the performances of the proposed techniques with the existing machine learning (ML) based methodologies. To achieve these objectives, firstly, a
modified ensemble ML classification technique has been proposed for PCOS detection with
patients’ symptom data utilizing state-of-the-art stacking technique with five traditional ML
models as base learners & one bagging or boosting ensemble model as meta-learner. At this
phase, three distinct types of feature selection methods are applied to explore the minimal &
optimal features for PCOS detection. Secondly, for PCOS prediction using ovary USG images, an extended ML classification technique has been proposed, trained & tested over 594
ovary USG images; where the Convolutional Neural Network (CNN) with different stateof-the-art techniques & transfer learning has been employed for feature extraction from the
images; then stacking ensemble machine learning technique using conventional models as
base learners & bagging or boosting ensemble model as meta-learner have been used on that
reduced feature set to classify between PCOS & non-PCOS ovaries. Finally, the comparative analysis has revealed that the proposed techniques for both cases significantly enhances
the predictive performances in comparison to the existing ML based techniques. In case of
symptom data, the proposed ensemble technique with ‘Gradient Boosting’ classifier as meta
learner outperforms others with 95.7% accuracy while using the features selected using PCA
method. Using the proposed technique with USG images, the best performing results are
obtained by incorporating the ‘VGGNet16’ pre-trained model with CNN architecture as
feature extractor and then stacking ensemble model with the meta-learner being ‘XGBoost’
model as image classifier with an accuracy of 99.89% for classification.
