PloS one · 2024
Predictive model for converting optic neuritis to multiple sclerosis; decision tree in focus.
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Rasouli Saeid, Dakkali Mohammad Sedigh, Ghazvini Azim, Azarbad Reza, Asani Mahdi, Mirzaasgari Zahra, Arish Mohammed
Аннотация
BACKGROUND: Optic neuritis (ON) can be an initial clinical presentation of multiple sclerosis This study aims to provide a practical predictive model for identifying at-risk ON patients in developing MS.
METHOD: We utilized data from the Optic Neuritis Treatment Trial study, which enrolled 457 patients aged from 18 to 46 years, all diagnosed with acute ON. These patients underwent up to 15 years of neurological and ophthalmologic examinations and imaging. The selection of variables for the developing model was based on clinical importance and statistical significance, and any missing values were appropriately addressed. We developed a Decision Tree (DT) classifier as the primary model and manually tuned its hyperparameters for optimal performance. We employed SHapley Additive exPlanations (SHAP) for feature importance evaluation. All analysis was performed using Python version 3.10.9 and its associated libraries.
RESULTS: A total of 388 patients completed the study, of which 154 developed clinically definite multiple sclerosis (CDMS). It was observed that 61% of patients with magnetic resonance imaging (MRI) lesions developed CDMS. The final variables selected for analysis were MRI lesions, neurologic history, ON type, gender, and visual field mean deviation. The DT model achieved an accuracy of 70.1% during cross-validation and 69.1% on the test set, with an area under the curve of 74.9% and 71.7%, respectively. Comparative analysis of DT with other models showed similar performance. SHAP analysis revealed that MRI lesions and ON type emerged as the two most significant features, with relative importance of 61% and 18%, respectively.
CONCLUSION: The decision tree model, with satisfactory performance, effectively stratifies patients, based on baseline findings and offers valuable insights for informed decision-making by physicians.
METHOD: We utilized data from the Optic Neuritis Treatment Trial study, which enrolled 457 patients aged from 18 to 46 years, all diagnosed with acute ON. These patients underwent up to 15 years of neurological and ophthalmologic examinations and imaging. The selection of variables for the developing model was based on clinical importance and statistical significance, and any missing values were appropriately addressed. We developed a Decision Tree (DT) classifier as the primary model and manually tuned its hyperparameters for optimal performance. We employed SHapley Additive exPlanations (SHAP) for feature importance evaluation. All analysis was performed using Python version 3.10.9 and its associated libraries.
RESULTS: A total of 388 patients completed the study, of which 154 developed clinically definite multiple sclerosis (CDMS). It was observed that 61% of patients with magnetic resonance imaging (MRI) lesions developed CDMS. The final variables selected for analysis were MRI lesions, neurologic history, ON type, gender, and visual field mean deviation. The DT model achieved an accuracy of 70.1% during cross-validation and 69.1% on the test set, with an area under the curve of 74.9% and 71.7%, respectively. Comparative analysis of DT with other models showed similar performance. SHAP analysis revealed that MRI lesions and ON type emerged as the two most significant features, with relative importance of 61% and 18%, respectively.
CONCLUSION: The decision tree model, with satisfactory performance, effectively stratifies patients, based on baseline findings and offers valuable insights for informed decision-making by physicians.