ILSHIP: An interpretable and predictive model for hypothyroidism. (March 2023)

• Record Type: Journal Article
• Authors: Liao, Bin; Liang, Jinming; Guo, Binglei; Jia, Xiaoyao; Lu, Jiarong; Zhang, Tao; Sun, Ruina
• Is Part Of: Computers in biology and medicine. Volume 154(2023)
• Publication Date: 2023-03
• Subjects: Machine learning -- Disease prediction -- Hypothyroidism -- Interpretability -- Feature selection -- Imbalance processing; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106578
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Hypothyroidism is one of the common endocrine diseases, and its incidence is increasing year by year. Due to the insidious nature of this disease, it often leads to delayed treatment and even misdiagnosis. This paper proposes ILSHIP, an interpretable predictive model for hypothyroidism, to reduce its diagnostic complexity as well as improve the predictive performance and interpretability of existing models. First, the ILSHIP prediction model was built based on label encoding, missing value processing, feature selection, and data enhancement of the dataset. Second, the comprehensive performance of ILSHIP was compared with twelve existing related study models and eleven mainstream models, such as XGBoost and MLP. The experimental results showed that, based on the optimal hyperparameters the ILSHIP model can achieve 99.392%, 99.437%, 99.348%, 99.381%, and 99.960% in accuracy, recall, specificity, F1, and AUC, respectively. The accuracy of the ILSHIP model was about 0.7%–15.4% higher than the existing models. By introducing the SHAP framework into the ILSHIP model, important features affecting hypothyroidism such as thyroid stimulating hormone (TSH) and free thyroxine index (FTI) were also identified, and the influencing factors for different individuals were finally analyzed to provide a basis for medical personnel to monitor the condition. Graphical abstract: Image 1 Highlights: The accuracy is about 0.7%–15.4% higher than the existing models. ILSHIP enhances interpretability from an overall and individual perspective. Key factors affecting the prevalence of hypothyroidism were identified. The exploration of optimal feature engineering promotes performance improvements.