Hagen, K. et al. The epidemiology of headache disorders: A face-to-face interview of participants in hunt4. J. Headache Pain 19, 1–6 (2018).
Google Scholar
Yao, C. et al. Burden of headache disorders in china, 1990–2017: Findings from the global burden of disease study 2017. J. Headache Pain 20, 1–11 (2019).
Google Scholar
Takeshima, T. et al. Prevalence, burden, and clinical management of migraine in china, japan, and south Korea: A comprehensive review of the literature. J. Headache Pain 20, 1–15 (2019).
Google Scholar
Wu, Q. et al. Determining the efficacy and safety of acupuncture for the preventive treatment of menstrual migraine: A protocol for a prisma-compliant systematic review and meta-analysis. J. Pain Res. 16, 101–109 (2023).
Google Scholar
Pacheco-Barrios, K. et al. Primary headache disorders in Latin America and the Aaribbean: A meta-analysis of population-based studies. Cephalalgia 43, 03331024221128265 (2023).
Google Scholar
Islam, J. et al. Modulation of trigeminal neuropathic pain by optogenetic inhibition of posterior hypothalamus in cci-ion rat. Sci. Rep. 13, 489 (2023).
Google Scholar
Safiri, S. et al. The burden of Parkinson’s disease in the middle east and north Africa region, 1990–2019: Results from the global burden of disease study 2019. BMC Public Health 23, 107 (2023).
Google Scholar
Barral, E., Martins Silva, E., García-Azorín, D., Viana, M. & Puledda, F. Differential diagnosis of visual phenomena associated with migraine: Spotlight on aura and visual snow syndrome. Diagnostics 13, 252 (2023).
Google Scholar
Hansen, J. M. & Charles, A. Differences in treatment response between migraine with aura and migraine without aura: Lessons from clinical practice and rcts. J. Headache Pain 20, 1–10 (2019).
Google Scholar
Khanal, S. et al. A systematic review of economic evaluations of pharmacological treatments for adults with chronic migraine. J. Headache Pain 23, 122 (2022).
Google Scholar
Cao, L. Data science: A comprehensive overview. ACM Comput. Surv. 50, 1–42 (2017).
Google Scholar
Ashraf, N. et al. Multi-label emotion classification of URDU tweets. PeerJ Comput. Sci. 8, e896 (2022).
Google Scholar
Khan, L., Amjad, A., Ashraf, N., Chang, H.-T. & Gelbukh, A. Urdu sentiment analysis with deep learning methods. IEEE Access 9, 97803–97812 (2021).
Google Scholar
Khan, L., Amjad, A., Ashraf, N. & Chang, H.-T. Multi-class sentiment analysis of URDU text using multilingual Bert. Sci. Rep. 12, 5436 (2022).
Google Scholar
Khan, L., Amjad, A., Afaq, K. M. & Chang, H.-T. Deep sentiment analysis using CNN-LSTM architecture of English and roman URDU text shared in social media. Appl. Sci. 12, 2694 (2022).
Google Scholar
Amjad, A., Khan, L. & Chang, H.-T. Semi-natural and spontaneous speech recognition using deep neural networks with hybrid features unification. Processes 9, 2286 (2021).
Google Scholar
Amjad, A., Khan, L., Ashraf, N., Mahmood, M. B. & Chang, H.-T. Recognizing semi-natural and spontaneous speech emotions using deep neural networks. IEEE Access 10, 37149–37163 (2022).
Google Scholar
Amjad, A., Khan, L. & Chang, H.-T. Effect on speech emotion classification of a feature selection approach using a convolutional neural network. PeerJ Comput. Sci. 7, e766 (2021).
Google Scholar
Amjad, A. & Khan, L. Data augmentation and deep neural networks for the classification of Pakistani racial speakers recognition. PeerJ Comput. Sci. 8, e1053 (2022).
Google Scholar
Liu, F., Bao, G., Yan, M. & Lin, G. A decision support system for primary headache developed through machine learning. PeerJ 10, e12743 (2022).
Google Scholar
Aggarwal, S. & Pandey, K. Early identification of PCOS with commonly known diseases: Obesity, diabetes, high blood pressure and heart disease using machine learning techniques. Expert Syst. Appl. 217, 119532 (2023).
Google Scholar
Saini, A., Meitei, A. & Singh, J. Machine learning in healthcare: A review. In Proceedings of the International Conference on Innovative Computing & Communication (ICICC) (2021).
Tam, C. S. et al. Combining structured and unstructured data in EMRS to create clinically-defined EMR-derived cohorts. BMC Med. Inform. Decis. Mak. 21, 1–10 (2021).
Google Scholar
Scheurwegs, E., Luyckx, K., Luyten, L., Daelemans, W. & Van den Bulcke, T. Data integration of structured and unstructured sources for assigning clinical codes to patient stays. J. Am. Med. Inform. Assoc. 23, e11–e19 (2016).
Google Scholar
Akila1, A., Parameswari, R. & Jayakumari, C. Big data in healthcare: Management, analysis, and future prospects. In Handbook of Intelligent Healthcare Analytics: Knowledge Engineering with Big Data Analytics 309–326 (2022).
Lutz, W. et al. Prospective evaluation of a clinical decision support system in psychological therapy. J. Consult. Clin. Psychol. 90, 90 (2022).
Google Scholar
Gulati, S., Guleria, K. & Goyal, N. Classification of migraine disease using supervised machine learning. In ’2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), 1–7 (organizationIEEE, 2022).
Aslan, Z. Deep convolutional neural network-based framework in the automatic diagnosis of migraine. Circuits Syst. Signal Process. 42(5), 3054–3071 (2022).
Google Scholar
Göker, H. Automatic detection of migraine disease from EEG signals using bidirectional long-short term memory deep learning model. Signal Image Video Process. 17(4), 1255–1263 (2022).
Google Scholar
Sanchez-Sanchez, P. A., García-González, J. R. & Rúa Ascar, J. M. Automatic migraine classification using artificial neural networks. F1000Research 9, 618 (2020).
Google Scholar
Zhu, B., Coppola, G. & Shoaran, M. Migraine classification using somatosensory evoked potentials. Cephalalgia 39, 1143–1155 (2019).
Google Scholar
Yang, H., Zhang, J., Liu, Q. & Wang, Y. Multimodal MRI-based classification of migraine: Using deep learning convolutional neural network. Biomed. Eng. Online 17, 1–14 (2018).
Google Scholar
Garcia-Chimeno, Y., Garcia-Zapirain, B., Gomez-Beldarrain, M., Fernandez-Ruanova, B. & Garcia-Monco, J. C. Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data. BMC Med. Inform. Decis. Mak. 17, 1–10 (2017).
Google Scholar
Jindal, K. et al. Migraine disease diagnosis from eeg signals using non-linear feature extraction technique. In ’2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 1–4 (organizationIEEE, 2018).
Chen, W.-T. et al. Migraine classification by machine learning with functional near-infrared spectroscopy during the mental arithmetic task. Sci. Rep. 12, 14590 (2022).
Google Scholar
Sah, R. D., Sheetlani, J., Kumar, D. R. & Sahu, I. N. Migraine (headaches) disease data classification using data mining classifiers. J. Res. Env. Earth Sci. 3, 10–16 (2017).
Pagán, J. et al. Robust and accurate modeling approaches for migraine per-patient prediction from ambulatory data. Sensors 15, 15419–15442 (2015).
Google Scholar
Chong, C. D. et al. Migraine classification using magnetic resonance imaging resting-state functional connectivity data. Cephalalgia 37, 828–844 (2017).
Google Scholar
Celik, U., Yurtay, N. & Pamuk, Z. Migraine diagnosis by using artificial neural networks and decision tree techniques. AJIT-e Acad. J. Inform. Technol. 5, 79–90 (2014).
Google Scholar
Ferroni, P. et al. Machine learning approach to predict medication overuse in migraine patients. Comput. Struct. Biotechnol. J. 18, 1487–1496 (2020).
Google Scholar
Krawczyk, B., Simić, D., Simić, S. & Woźniak, M. Automatic diagnosis of primary headaches by machine learning methods. Open Med. 8, 157–165 (2013).
Google Scholar
Chen, I. Y. et al. Ethical machine learning in healthcare. Ann. Rev. Biomed. Data Sci. 4, 123–144 (2021).
Google Scholar
Akben, S. B., Tuncel, D. & Alkan, A. Classification of multi-channel eeg signals for migraine detection. Biomed. Res. 27, 743–748 (2016).
Akben, S. B., Subasi, A. & Tuncel, D. Analysis of repetitive flash stimulation frequencies and record periods to detect migraine using artificial neural network. J. Med. Syst. 36, 925–931 (2012).
Google Scholar
Subasi, A., Ahmed, A., Aličković, E. & Hassan, A. R. Effect of photic stimulation for migraine detection using random forest and discrete wavelet transform. Biomed. Signal Process. Control 49, 231–239 (2019).
Google Scholar
Casas Pulido, A. F., Hernandez Cely, M. M. & Rodriguez, O. M. H. Análisis experimental de flujo líquido-líquido en un tubo horizontal usando redes neuronales artificiales. Revista UIS Ingenierías 22, 49–56 (2023).
Google Scholar
Dumkrieger, G., Chong, C. D., Ross, K., Berisha, V. & Schwedt, T. J. The value of brain MRI functional connectivity data in a machine learning classifier for distinguishing migraine from persistent post-traumatic headache. Front. Pain Res. 3, 1012831 (2023).
Google Scholar
Nie, W., Zeng, W., Yang, J., Zhao, L. & Shi, Y. Classification of migraine using static functional connectivity strength and dynamic functional connectome patterns: A resting-state fmri study. Brain Sci. 13, 596 (2023).
Google Scholar
Marino, S. et al. Classifying migraine using pet compressive big data analytics of brain’s \(\mu\)-opioid and d2/d3 dopamine neurotransmission. Front. Pharmacol. 14, 1173596 (2023).
Google Scholar
Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. Smote: Synthetic minority over-sampling technique. J. Art. Intell. Res. 16, 321–357 (2002).
Uddin, S., Khan, A., Hossain, M. E. & Moni, M. A. Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Mak. 19, 1–16 (2019).
Google Scholar
Mitrović, K., Petrušić, I., Radojičić, A., Daković, M. & Savić, A. Migraine with aura detection and subtype classification using machine learning algorithms and morphometric magnetic resonance imaging data. Front. Neurol. 14, 1106612 (2023).
Google Scholar
Kwon, J. et al. Machine learning-based automated classification of headache disorders using patient-reported questionnaires. Sci. Rep. 10, 14062 (2020).
Google Scholar
Doupe, P., Faghmous, J. & Basu, S. Machine learning for health services researchers. Value Health 22, 808–815 (2019).
Google Scholar
Waring, J., Lindvall, C. & Umeton, R. Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artif. Intell. Med. 104, 101822 (2020).
Google Scholar
