Evolutionary algorithms and decision trees for predicting poor outcome after endovascular treatment for acute ischemic stroke. (June 2021)

• Record Type: Journal Article
• Authors: Kappelhof, N.; Ramos, L.A.; Kappelhof, M.; van Os, H.J.A.; Chalos, V.; van Kranendonk, K.R.; Kruyt, N.D.; Roos, Y.B.W.E.M.; van Zwam, W.H.; van der Schaaf, I.C.; van Walderveen, M.A.A.; Wermer, M.J.H.; van Oostenbrugge, R.J.; Lingsma, Hester; Dippel, Diederik; Majoie, C.B.L.M.; Marquering, H.A.
• Is Part Of: Computers in biology and medicine. Volume 133(2021)
• Publication Date: 2021-06
• Subjects: Acute ischemic stroke -- Machine learning -- Prognostics -- Decision trees -- Evolutionary algorithms -- Fuzzy -- Endovascular treatment; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104414
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Despite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs in almost one-third of patients. These patients, who might not benefit from treatment, have been previously identified with traditional logistic regression models, which may oversimplify relations between characteristics and outcome, or machine learning techniques, which may be difficult to interpret. We developed and evaluated a novel evolutionary algorithm for fuzzy decision trees to accurately identify patients with poor outcome after endovascular treatment, which was defined as having a modified Rankin Scale score ( mRS ) higher or equal to 5. The created decision trees have the benefit of being comprehensible, easily interpretable models, making its predictions easy to explain to patients and practitioners. Insights in the reason for the predicted outcome can encourage acceptance and adaptation in practice and help manage expectations after treatment. We compared our proposed method to CART, the benchmark decision tree algorithm, on classification accuracy and interpretability. The fuzzy decision tree significantly outperformed CART: using 5-fold cross-validation with on average 1090 patients in the training set and 273 patients in the test set, the fuzzy decision tree misclassified on average 77 (standard deviation of 7) patients compared to 83 ( ± 7) using CART. The mean number of nodes (decision and leaf nodes) in the fuzzy decision tree was 11 ( ± 2) compared to 26 ( ± 1) for CART decision trees. With an average accuracy of 72% and much fewer nodes than CART, the developed evolutionary algorithm for fuzzy decision trees might be used to gain insights into the predictive value of patient characteristics and can contribute to the development of more accurate medical outcome prediction methods with improved clarity for practitioners and patients. Highlights: Novel algorithm to develop understandable yet powerful decision trees based on evolutionary methods. Concept of fuzzy splits in decision trees introduces flexibility when splitting on numerical or ordinal variables. Outperforms benchmark CART in predicting functional outcome of endovascular treatment for acute ischemic stroke patients.