A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome

Abstract

Polycystic Ovary Syndrome (PCOS) is a complex disorder predominantly defined by biochemical hyperandrogenism, oligomenorrhea, anovulation, and in some cases, the presence of ovarian microcysts. This endocrinopathy inhibits ovarian follicle development causing symptoms like obesity, acne, infertility, and hirsutism. Artificial Intelligence (AI) has revolutionized healthcare, contributing remarkably to science and engineering domains. Therefore, we have demonstrated an AI approach using heterogeneous Machine Learning (ML) and Deep Learning (DL) classifiers to predict PCOS among fertile patients. We used an Open-source dataset of 541 patients from Kerala, India. Among all the classifiers, the final multi-stack of ML models performed best with accuracy, precision, recall, and F1-score of 98%, 97%, 98%, and 98%. Explainable AI (XAI) techniques make model predictions understandable, interpretable, and trustworthy. Hence, we have utilized XAI techniques such as SHAP (SHapley Additive Values), LIME (Local Interpretable Model Explainer), ELI5, Qlattice, and feature importance with Random Forest for explaining tree-based classifiers. The motivation of this study is to accurately detect PCOS in patients while simultaneously proposing an automated screening architecture with explainable machine learning tools to assist medical professionals in decision-making.

DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images

“…Our method offers a full view of a particular site, which enables us to identify the disease, as well as interior areas that have been infected with it. Dermoscopy is the most reliable [ 41 ] and time-effective [ 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ] approach for determining if a lesion is a BCC, MEL, SCC, or MN. A computerized diagnostic approach is required to identify BCC, MEL, SCC, and MN, since the number of confirmed cases of deadly skin cancer is continually growing [ 62 ].…”

“…These images were represented by bkl, mel, and nv. Krishnaraj et al [ 52 ] designed machine learning [ 53 , 54 , 55 , 56 ] classifiers that identified binary classes of cervical cancer, such as adenosquamous carcinoma and SCC. They collected the dataset at the University of California, Irvine (UCI) repository, and the Borderline-SMOTE approach was employed to balance the unbalanced data.…”

A machine learning and explainable artificial intelligence triage-prediction system for COVID-19

“…On model evaluation, it was observed that the tree-based models trained by Borderline SMOTE were more reliable than other deployed models. In this research, we aim to create classification pipelines and provide interpretability for the predictions made [34] . For explaining various classifiers, we utilized various XAI tools such as Shapley Additive Explanations, Local Interpretable Model-Agonistic Explanations, Quantum Lattice, ELI5, and Anchor.…”

Polycystic Ovary Syndrome Detection Machine Learning Model Based on Optimized Feature Selection and Explainable Artificial Intelligence

“…In [ 22 ], DT with Gini importance recorded an ACC of 92.59. In [ 25 ], multi-stack of ML recorded an ACC of 98.…”

“…In [ 25 ], the authors used ML models: LR, DT, RF, SVM, NB, KNN, AdaBoost, XGBoost, and Extratrees and DL and proposed multi-stacking ML to predict PCOS. They used Explainable AI (XAI) techniques to make model predictions understandable, interpretable, and trustworthy.…”