Interpretable heart disease risk prediction via FCA-constrained logistic regression.
Objective: To develop an interpretable and clinically coherent heart disease risk prediction model by integrating Formal Concept Analysis (FCA) with a novel closure-constrained logistic regression that enforces coefficient coherence within FCA-derived concepts. Methods: We used the Heart Disease Hea...
| Published in: | Health Informatics Journal Vol. 32; no. 2; pp. 1 - 15 |
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| Main Authors: | , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Sage Publications Inc.
Apr-Jun2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194971581&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194971581 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14604582 EJK jtl: Health Informatics Journal issn: 14604582 maglogo: Y pubinfo: dt: Apr-Jun2026 vid: 32 iid: 2 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 194971581 194971581 194971581 10.1177/14604582261444612 194971581 ppf: 1 ppct: 14 formats: tig: atl: Interpretable heart disease risk prediction via FCA-constrained logistic regression. aug: au: Salehi, Arman Heydarian, Ashkan Goudarzi, Hamid Reza Rad, Zahra Farzin affil: Department of Computer Engineering, SR.C, Islamic Azad University, Tehran, Iran sug: subj: Heart Diseases Diagnosis Heart Diseases Risk Factors Prediction Models Concept Analysis Logistic Regression Risk Assessment Human Retrospective Design Record Review Cross Sectional Studies Classification Algorithms Prediction Algorithms Random Forest Artificial Intelligence Predictive Value of Tests Decision Support Systems, Clinical Reproducibility of Results Sensitivity and Specificity ROC Curve Descriptive Statistics ab: Objective: To develop an interpretable and clinically coherent heart disease risk prediction model by integrating Formal Concept Analysis (FCA) with a novel closure-constrained logistic regression that enforces coefficient coherence within FCA-derived concepts. Methods: We used the Heart Disease Health Indicators dataset (BRFSS 2015; N≈380,000). Predictors were discretized into binary attributes, and closed itemsets were extracted via FCA. A closure penalty, which minimizes within-concept coefficient variance, was added to the logistic regression objective. Hyperparameters (closure strength λ, FCA minimum support) were selected using five-fold cross-validation on the training set. Baselines included L2-regularized logistic regression, Random Forest, and Gradient Boosting. Performance was evaluated on a held-out test set using AUC, accuracy, precision, recall, F1, PR-AUC, and Brier Score. Results: On the held-out test set, the FCA-constrained model achieved Accuracy = 0.906, AUC = 0.810, Precision = 0.709, Recall = 0.544, F1 = 0.556, PR-AUC = 0.265, and Brier Score = 0.078. Compared to baselines, the FCA model produced well-calibrated probabilities and the highest precision and F1-score, while providing concept-level explanations grounded in clinically coherent closed itemsets. Conclusion: Embedding FCA structure directly into model training yields an interpretable linear model with competitive discrimination and improved precision at clinically relevant thresholds. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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