Patrizia Ferroni: BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy.
Fabio M Zanzotto: Department of Enterprise Engineering, University of Rome "Tor Vergata", Viale Oxford 81, 00133 Rome, Italy.
Noemi Scarpato: Dept. of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, Via di Val Cannuta 247, 00166 Rome, Italy.
Antonella Spila: BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy.
Luisa Fofi: Headache and Pain Unit, Dept. of Neurological, Motor and Sensorial Sciences, IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy.
Gabriella Egeo: Headache and Pain Unit, Dept. of Neurological, Motor and Sensorial Sciences, IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Rome, Italy.
Machine learning (ML) is largely used to develop automatic predictors in migraine classification but automatic predictors for medication overuse (MO) in migraine are still in their infancy. Thus, to understand the benefits of ML in MO prediction, we explored an automated predictor to estimate MO risk in migraine. To achieve this objective, a study was designed to analyze the performance of a customized ML-based decision support system that combines support vector machines and Random Optimization (RO-MO). We used RO-MO to extract prognostic information from demographic, clinical and biochemical data. Using a dataset of 777 consecutive migraine patients we derived a set of predictors with discriminatory power for MO higher than that observed for baseline SVM. The best four were incorporated into the final RO-MO decision support system and risk evaluation on a five-level stratification was performed. ROC analysis resulted in a c-statistic of 0.83 with a sensitivity and specificity of 0.69 and 0.87, respectively, and an accuracy of 0.87 when MO was predicted by at least three RO-MO models. Logistic regression analysis confirmed that the derived RO-MO system could effectively predict MO with ORs of 5.7 and 21.0 for patients classified as probably (3 predictors positive), or definitely at risk of MO (4 predictors positive), respectively. In conclusion, a combination of ML and RO - taking into consideration clinical/biochemical features, drug exposure and lifestyle - might represent a valuable approach to MO prediction in migraine and holds the potential for improving model precision through weighting the relative importance of attributes.
