Investigation on explainable machine learning models to predict chronic kidney diseases

Abstract

Chronic kidney disease (CKD) is a major worldwide health problem, affecting a large proportion of the world’s population and leading to higher morbidity and death rates. The early stages of CKD sometimes present without visible symptoms, causing patients to be unaware. Early detection and treatments are critical in reducing complications and improving the overall quality of life for people afflicted. In this work, we investigate the use of an explainable artificial intelligence (XAI)-based strategy, leveraging clinical characteristics, to predict CKD. This study collected clinical data from 491 patients, comprising 56 with CKD and 435 without CKD, encompassing clinical, laboratory, and demographic variables. To develop the predictive model, five machine learning (ML) methods, namely logistic regression (LR), random forest (RF), decision tree (DT), Naïve Bayes (NB), and extreme gradient boosting (XGBoost), were employed. The optimal model was selected based on accuracy and area under the curve (AUC). Additionally, the SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) algorithms were utilized to demonstrate the influence of the features on the optimal model. Among the five models developed, the XGBoost model achieved the best performance with an AUC of 0.9689 and an accuracy of 93.29%. The analysis of feature importance revealed that creatinine, glycosylated hemoglobin type A1C (HgbA1C), and age were the three most influential features in the XGBoost model. The SHAP force analysis further illustrated the model’s visualization of individualized CKD predictions. For further insights into individual predictions, we also utilized the LIME algorithm. This study presents an interpretable ML-based approach for the early prediction of CKD. The SHAP and LIME methods enhance the interpretability of ML models and help clinicians better understand the rationale behind the predicted outcomes more effectively.

Explainable machine learning on baseline MRI predicts multiple sclerosis trajectory descriptors

“…Through a quantitative approach to assess the marginal impact of features, SHAP considers all feature combinations [ 23 ]. This facilitates a thorough comprehension of feature interactions and their combined impacts on predictions [ 24 ]. The mathematical expression representing SHAP values is given by Eq 4 .…”

XAI-based Clinical Decision Support System: A Systematic Review

“…A hybrid approach combining Vision Transformer (ViT) and a Gated Recurrent Unit) was used to generate LIME heat maps using the top three features from the brain MRI images, and SHAP was used to visualize the model's predictions to demonstrate the validity of data patterns [90]. In addition, the Department of Chronic Kidney Disease also used LIME and SHAP algorithms simultaneously to represent the importance of features in the best model trained by five machine learning methods methods (Random Forest, Decision Tree, Naïve Bayes, XGBoost, and Logistic Regression) [91].…”