# A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome
## 5 Discussion  
Polycystic Ovary Syndrome (PCOS) is a hormonal disorder that causes ovary enlargement and symptoms such as obesity, acne, hirsutism, oligomenorrhea, acanthosis nigricans, and male-pattern baldness. It is often underdiagnosed or misdiagnosed, leading to the risk of severe health conditions such as type 2 diabetes, hypertension, cardiovascular disorders, infertility, and uterine and endometrial cancer. We created multiple ML and DL pipelines in this study and evaluated their performance to find the best-performing classifier. The ML frameworks were created by considering all combinations of two data split ratios, three feature extraction methods, and 10 classifiers. Models trained with 80% (80:20 split) of data samples performed significantly better than the ones trained on 70% of samples, regardless of the feature selection technique. As seen in Table 5, the impact of various feature selection techniques gives insights into the training of the classifiers. Interestingly, all tree-based classifiers such as DT, RF, AdaBoost, XGBoost, and Extra-trees had comparable performances irrespective of the features selected. However, Classifiers trained on MI-extracted features achieved the highest performance metrics among other deployed feature selection techniques. These models had higher sensitivity, assuring a significant reduction in false negatives. Further, the accuracies of most models trained on the HHO and SSA methods provided a similar performance, except for the NB classifier, whose accuracy significantly worsened with SSA data. Stack models, a blend of individual meta-learners based on how they get trained, would define how they work in an ensemble. This can be observed in the STACK-3 performance under each feature selection technique. HHO features trained the poorest STACK-3 classifier with an accuracy of 92%, and MI features trained the best stack achieving 98% accuracy. Figure 4 depicts the improvement in the AUC-ROC score and PR curve for STACK-3 from HHO to MI-trained stack. Different feature-selection pipelines had different 'best-performing' classifiers. With HHO, the stochastic-based method for feature selection, XGBoost outperformed all other models with prediction accuracy, recall, and AUC score of 97%, 100%, and 99%, respectively. When considering the model performance on data feature engineering with SSA, STACK-2 achieved the best accuracy, recall, and AUC score of 98%, 98%, and 99%, respectively. Classifiers trained on MI-extracted features achieved the highest performance metrics among other deployed feature selection techniques. Out of all combinations of classifiers and feature extraction techniques, STACK-3 trained with an 80:20 split on MI-engineered data had the highest performance, achieving accuracy, precision, recall, f1-score, AUC score, and Precision-Recall scores of 98%, 97%, 98%, 98%, 1, and 1, respectively. As indicated in Table 6, it should be noted meta-learners like RF, SVM-RBF, and XGBoost trained on an 80:20 split on MI-engineered data have performed significantly better than other models, with close to 100% sensitivity. These meta-learners could be the ideal choice when designing individual PCOS classifiers. The ML-based pipelines were compared with customized DL architectures. DNN obtaining accuracy and recall of 94% and 98%, respectively. 1-D CNN performed comparatively poor with an accuracy and recall of 90% and 94%. Both the tree-based meta-learner (MI) pipelines such as RF, XGBoost and STACK-3 have outperformed these complex deep learning architectures. Our proposed best-performing multi-level stack drastically lowered the number of false negatives PCOS-predictions (with only 3 out of the processed 146 samples). Further, we added a layer of Explainable machine learning to the high-performing tree-based meta-learner frameworks. We explored multiple SHAP with visualizations to explain global and local XGBoost predictions based on feature importance. LIME created an intuitive and interpretable probability-based visual for local predictions of Random Forest.