A decision support system for primary headache developed through machine learning

Abstract

Background Primary headache is a disorder with a high incidence and low diagnostic accuracy; the incidence of migraine and tension-type headache ranks first among primary headaches. Artificial intelligence (AI) decision support systems have shown great potential in the medical field. Therefore, we attempt to use machine learning to build a clinical decision-making system for primary headaches. Methods The demographic data and headache characteristics of 173 patients were collected by questionnaires. Decision tree, random forest, gradient boosting algorithm and support vector machine (SVM) models were used to construct a discriminant model and a confusion matrix was used to calculate the evaluation indicators of the models. Furthermore, we have carried out feature selection through univariate statistical analysis and machine learning. Results In the models, the accuracy, F1 score were calculated through the confusion matrix. The logistic regression model has the best discrimination effect, with the accuracy reaching 0.84 and the area under the ROC curve also being the largest at 0.90. Furthermore, we identified the most important factors for distinguishing the two disorders through statistical analysis and machine learning: nausea/vomiting and photophobia/phonophobia. These two factors represent potential independent factors for the identification of migraines and tension-type headaches, with the accuracy reaching 0.74 and the area under the ROC curve being at 0.74. Conclusions Applying machine learning to the decision-making system for primary headaches can achieve a high diagnostic accuracy. Among them, the discrimination effect obtained by the integrated algorithm is significantly better than that of a single learner. Second, nausea/vomiting, photophobia/phonophobia may be the most important factors for distinguishing migraine from tension-type headaches.

Machine‐learning‐based approach for predicting response to anti‐calcitonin gene‐related peptide (CGRP) receptor or ligand antibody treatment in patients with migraine: A multicenter Spanish study

“…ML‐based models have demonstrated the ability to predict MOH and OnabotulinumtoxinA treatment response in patients with migraine, improving model precision through weighting the relative importance of attributes [23, 42]. Moreover, ML models have been highlighted as tools that can serve as a decision support system [43].…”

Verification of a clinical decision support system for the diagnosis of headache disorders based on patient–computer interactions: a multi-center study

“…Although there are large differences between typical migraines and TTH, the symptoms of most TTH patients are not typical, particularly in cases of TTH and migraine without aura [ 14 ]. A machine learning study identified factors for distinguishing migraine from TTH [ 15 ]. In the present study, headache specialists differentially diagnosed PM or PTTH based on their clinical experience, although there is overlap between migraine and TTH in individual patients.…”

Migraine headache (MH) classification using machine learning methods with data augmentation

“…In Study 20 , the primary focus of the authors was to design and implement a decision support system for diagnosing tension-type and migraine headaches using machine learning. The logistic regression model achieved the best results with an accuracy of 0.84 out of other models such as gradient boosting algorithms and random forest.…”

“…Therefore, an automatic affordable, accessible approach is required for migraine classification and prediction. Fortunately, machine learning algorithms show state-of-the-art performance in tasks such as text classification 12 – 15 speech recognition 16 – 19 , and many health-related automatic disease classifications and predictions 20 , 21 .…”