Global interpretation of machine learning models in predicting polycystic ovary syndrome with the explainable artificial intelligence method SHAP
Küçük Resim Yok
Tarih
2024
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Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Polycystic ovary syndrome (PCOS) is a complex condition characterized by high male hormone levels, irregular menstrual cycles, lack of ovulation, and sometimes small ovarian cysts. Often underdiagnosed, PCOS leads to significant health issues, making timely and efficient identification crucial. Recently, machine learning (ML) has shown promise in medical diagnoses, but the perceived \"black box\" nature of ML models necessitates explanations of key parameters influencing predictions. This study aims to provide global explanations using SHapley Additive exPlanations (SHAP) to ensure the efficiency, effectiveness, and reliability of the ML model. An open-access dataset with 300 PCOS patients was utilized to predict whether a patient's luteinizing hormone (LH) to follicle-stimulating hormone (FSH) ratio is up to 1 or more than 1. The study employed ML classifiers including AdaBoost, XGBoost, CatBoost, and Bagging methods, with Bagging performing the best. The modeling process used a 5-fold cross-validation approach, splitting the dataset into 80% training and 20% testing sets. The model's performance was evaluated using accuracy (ACC), balanced accuracy (b-ACC), specificity (SP), sensitivity (SE), negative predictive value (npv), positive predictive value (ppv), and F1-score. The Bagging method yielded the following performance metrics: ACC (99.0%), b-ACC (99.0%), SE (98.8%), SP (99.1%), ppv (97.7%), npv (99.5%), and F1-score (98.3%). SHAP analysis identified the top predictors for distinguishing between LH: FSH ratio categories as TTng/dL, BMI, AMH, age, family history, and menstrual cycle regulation. This study demonstrates that incorporating SHAP explanations enhances the interpretability and reliability of ML models in diagnosing PCOS.
Açıklama
Anahtar Kelimeler
Tıbbi Araştırmalar Deneysel, Kadın Hastalıkları ve Doğum, Bilgisayar Bilimleri, Yapay Zeka
Kaynak
Medicine Science
WoS Q Değeri
Scopus Q Değeri
Cilt
13
Sayı
3
