XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease

• Published in: BMC medical informatics and decision making 2023-07, Vol.23 (1), p.137-14, Article 137
• Main Authors: Yi, Fuliang, Yang, Hui, Chen, Durong, Qin, Yao, Han, Hongjuan, Cui, Jing, Bai, Wenlin, Ma, Yifei, Zhang, Rong, Yu, Hongmei
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Analysis; Apolipoprotein E; Bagging; Bayes Theorem; Biomarkers; Brain research; Care and treatment; Classification; Cognition; Cognition & reasoning; Cognitive ability; Cognitive Dysfunction - diagnosis; Data integrity; Datasets; Decision making; Decision trees; Deep learning; Diagnosis; Diagnostic imaging; Discriminant analysis; Evaluation; Humans; Imbalanced classes; Interpretable framework; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical diagnosis; Medical imaging; Multiclassification; Neural networks; Neurodegenerative diseases; Neuroimaging; Neuropsychology; Numerical prediction; Optimization; Ventricle; Ventricles (cerebral); XGBoost-SHAP
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-023-02238-9

Due to the class imbalance issue faced when Alzheimer's disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD. We obtained patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-ε4 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer's Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset. Compared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%/74.85%), specificity (92.18%/89.86%), accuracy (87.57%/80.52%), area under the receiver operating characteristic curve (AUC) (0.91/0.88), positive clinical utility index (0.71/0.56), and negative clinical utility index (0.75/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of CDRSB, ADAS13, ADAS11, ventricle volume, ADASQ4, and FAQ were associated with higher risks