Hagen, Ok. et al. The epidemiology of headache problems: A face-to-face interview of individuals in hunt4. J. Headache Pain 19, 1–6 (2018).Article
Google Scholar
Yao, C. et al. Burden of headache problems in china, 1990–2017: Findings from the worldwide burden of illness research 2017. J. Headache Pain 20, 1–11 (2019).Article
Google Scholar
Takeshima, T. et al. Prevalence, burden, and scientific administration of migraine in china, japan, and south Korea: A complete evaluation of the literature. J. Headache Pain 20, 1–15 (2019).Article
Google Scholar
Wu, Q. et al. Determining the efficacy and security of acupuncture for the preventive remedy of menstrual migraine: A protocol for a prisma-compliant systematic evaluation and meta-analysis. J. Pain Res. 16, 101–109 (2023).Article
ADS
PubMed
PubMed Central
Google Scholar
Pacheco-Barrios, Ok. et al. Primary headache problems in Latin America and the Aaribbean: A meta-analysis of population-based research. Cephalalgia 43, 03331024221128265 (2023).Article
Google Scholar
Islam, J. et al. Modulation of trigeminal neuropathic ache by optogenetic inhibition of posterior hypothalamus in cci-ion rat. Sci. Rep. 13, 489 (2023).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Safiri, S. et al. The burden of Parkinson’s illness within the center east and north Africa area, 1990–2019: Results from the worldwide burden of illness research 2019. BMC Public Health 23, 107 (2023).Article
PubMed
PubMed Central
Google Scholar
Barral, E., Martins Silva, E., García-Azorín, D., Viana, M. & Puledda, F. Differential prognosis of visible phenomena related with migraine: Spotlight on aura and visible snow syndrome. Diagnostics 13, 252 (2023).Article
PubMed
PubMed Central
Google Scholar
Hansen, J. M. & Charles, A. Differences in remedy response between migraine with aura and migraine with out aura: Lessons from scientific follow and rcts. J. Headache Pain 20, 1–10 (2019).Article
Google Scholar
Khanal, S. et al. A scientific evaluation of financial evaluations of pharmacological therapies for adults with power migraine. J. Headache Pain 23, 122 (2022).Article
CAS
PubMed
PubMed Central
Google Scholar
Cao, L. Data science: A complete overview. ACM Comput. Surv. 50, 1–42 (2017).Article
CAS
Google Scholar
Ashraf, N. et al. Multi-label emotion classification of URDU tweets. PeerJ Comput. Sci. 8, e896 (2022).Article
PubMed
PubMed Central
Google Scholar
Khan, L., Amjad, A., Ashraf, N., Chang, H.-T. & Gelbukh, A. Urdu sentiment evaluation with deep learning methods. IEEE Access 9, 97803–97812 (2021).Article
Google Scholar
Khan, L., Amjad, A., Ashraf, N. & Chang, H.-T. Multi-class sentiment evaluation of URDU textual content using multilingual Bert. Sci. Rep. 12, 5436 (2022).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Khan, L., Amjad, A., Afaq, Ok. M. & Chang, H.-T. Deep sentiment evaluation using CNN-LSTM structure of English and roman URDU textual content shared in social media. Appl. Sci. 12, 2694 (2022).Article
CAS
Google Scholar
Amjad, A., Khan, L. & Chang, H.-T. Semi-natural and spontaneous speech recognition using deep neural networks with hybrid options unification. Processes 9, 2286 (2021).Article
Google Scholar
Amjad, A., Khan, L., Ashraf, N., Mahmood, M. B. & Chang, H.-T. Recognizing semi-natural and spontaneous speech feelings using deep neural networks. IEEE Access 10, 37149–37163 (2022).Article
Google Scholar
Amjad, A., Khan, L. & Chang, H.-T. Effect on speech emotion classification of a characteristic choice strategy using a convolutional neural community. PeerJ Comput. Sci. 7, e766 (2021).Article
PubMed
PubMed Central
Google Scholar
Amjad, A. & Khan, L. Data augmentation and deep neural networks for the classification of Pakistani racial audio system recognition. PeerJ Comput. Sci. 8, e1053 (2022).Article
PubMed
PubMed Central
Google Scholar
Liu, F., Bao, G., Yan, M. & Lin, G. A call help system for main headache developed by means of machine learning. PeerJ 10, e12743 (2022).Article
PubMed
PubMed Central
Google Scholar
Aggarwal, S. & Pandey, Ok. Early identification of PCOS with generally recognized ailments: Obesity, diabetes, hypertension and coronary heart illness using machine learning strategies. Expert Syst. Appl. 217, 119532 (2023).Article
Google Scholar
Saini, A., Meitei, A. & Singh, J. Machine learning in healthcare: A evaluation. 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).Article
Google Scholar
Scheurwegs, E., Luyckx, Ok., Luyten, L., Daelemans, W. & Van den Bulcke, T. Data integration of structured and unstructured sources for assigning scientific codes to affected person stays. J. Am. Med. Inform. Assoc. 23, e11–e19 (2016).Article
PubMed
Google Scholar
Akila1, A., Parameswari, R. & Jayakumari, C. Big data in healthcare: Management, evaluation, and future prospects. In Handbook of Intelligent Healthcare Analytics: Knowledge Engineering with Big Data Analytics 309–326 (2022).Lutz, W. et al. Prospective analysis of a scientific determination help system in psychological remedy. J. Consult. Clin. Psychol. 90, 90 (2022).Article
PubMed
Google Scholar
Gulati, S., Guleria, Ok. & Goyal, N. Classification of migraine illness using supervised machine learning. In ’2022 tenth 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 within the computerized prognosis of migraine. Circuits Syst. Signal Process. 42(5), 3054–3071 (2022).Article
Google Scholar
Göker, H. Automatic detection of migraine illness from EEG alerts using bidirectional long-short time period reminiscence deep learning mannequin. Signal Image Video Process. 17(4), 1255–1263 (2022).Article
Google Scholar
Sanchez-Sanchez, P. A., García-González, J. R. & Rúa Ascar, J. M. Automatic migraine classification using synthetic neural networks. F1000Research 9, 618 (2020).Article
PubMed
PubMed Central
Google Scholar
Zhu, B., Coppola, G. & Shoaran, M. Migraine classification using somatosensory evoked potentials. Cephalalgia 39, 1143–1155 (2019).Article
PubMed
Google Scholar
Yang, H., Zhang, J., Liu, Q. & Wang, Y. Multimodal MRI-based classification of migraine: Using deep learning convolutional neural community. Biomed. Eng. Online 17, 1–14 (2018).Article
Google Scholar
Garcia-Chimeno, Y., Garcia-Zapirain, B., Gomez-Beldarrain, M., Fernandez-Ruanova, B. & Garcia-Monco, J. C. Automatic migraine classification through characteristic choice committee and machine learning strategies over imaging and questionnaire data. BMC Med. Inform. Decis. Mak. 17, 1–10 (2017).Article
Google Scholar
Jindal, Ok. et al. Migraine illness prognosis from eeg alerts using non-linear characteristic extraction method. 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 purposeful near-infrared spectroscopy throughout the psychological arithmetic process. Sci. Rep. 12, 14590 (2022).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Sah, R. D., Sheetlani, J., Kumar, D. R. & Sahu, I. N. Migraine (complications) illness data classification using data mining classifiers. J. Res. Env. Earth Sci. 3, 10–16 (2017).
Google Scholar
Pagán, J. et al. Robust and correct modeling approaches for migraine per-patient prediction from ambulatory data. Sensors 15, 15419–15442 (2015).Article
ADS
PubMed
PubMed Central
Google Scholar
Chong, C. D. et al. Migraine classification using magnetic resonance imaging resting-state purposeful connectivity data. Cephalalgia 37, 828–844 (2017).Article
PubMed
Google Scholar
Celik, U., Yurtay, N. & Pamuk, Z. Migraine prognosis by using synthetic neural networks and determination tree strategies. AJIT-e Acad. J. Inform. Technol. 5, 79–90 (2014).Article
Google Scholar
Ferroni, P. et al. Machine learning strategy to foretell medicine overuse in migraine sufferers. Comput. Struct. Biotechnol. J. 18, 1487–1496 (2020).Article
CAS
PubMed
PubMed Central
Google Scholar
Krawczyk, B., Simić, D., Simić, S. & Woźniak, M. Automatic prognosis of main complications by machine learning methods. Open Med. 8, 157–165 (2013).Article
Google Scholar
Chen, I. Y. et al. Ethical machine learning in healthcare. Ann. Rev. Biomed. Data Sci. 4, 123–144 (2021).Article
Google Scholar
Akben, S. B., Tuncel, D. & Alkan, A. Classification of multi-channel eeg alerts for migraine detection. Biomed. Res. 27, 743–748 (2016).
Google Scholar
Akben, S. B., Subasi, A. & Tuncel, D. Analysis of repetitive flash stimulation frequencies and report intervals to detect migraine using synthetic neural community. J. Med. Syst. 36, 925–931 (2012).Article
PubMed
Google Scholar
Subasi, A., Ahmed, A., Aličković, E. & Hassan, A. R. Effect of photic stimulation for migraine detection using random forest and discrete wavelet rework. Biomed. Signal Process. Control 49, 231–239 (2019).Article
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).Article
Google Scholar
Dumkrieger, G., Chong, C. D., Ross, Ok., Berisha, V. & Schwedt, T. J. The worth of mind MRI purposeful connectivity data in a machine learning classifier for distinguishing migraine from persistent post-traumatic headache. Front. Pain Res. 3, 1012831 (2023).Article
Google Scholar
Nie, W., Zeng, W., Yang, J., Zhao, L. & Shi, Y. Classification of migraine using static purposeful connectivity energy and dynamic purposeful connectome patterns: A resting-state fmri research. Brain Sci. 13, 596 (2023).Article
PubMed
PubMed Central
Google Scholar
Marino, S. et al. Classifying migraine using pet compressive large data analytics of mind’s (mu)-opioid and d2/d3 dopamine neurotransmission. Front. Pharmacol. 14, 1173596 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Chawla, N. V., Bowyer, Ok. W., Hall, L. O. & Kegelmeyer, W. P. Smote: Synthetic minority over-sampling method. J. Art. Intell. Res. 16, 321–357 (2002).
Google Scholar
Uddin, S., Khan, A., Hossain, M. E. & Moni, M. A. Comparing totally different supervised machine learning algorithms for illness prediction. BMC Med. Inform. Decis. Mak. 19, 1–16 (2019).Article
Google Scholar
Mitrović, Ok., 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).Article
PubMed
PubMed Central
Google Scholar
Kwon, J. et al. Machine learning-based automated classification of headache problems using patient-reported questionnaires. Sci. Rep. 10, 14062 (2020).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Doupe, P., Faghmous, J. & Basu, S. Machine learning for well being providers researchers. Value Health 22, 808–815 (2019).Article
PubMed
Google Scholar
Waring, J., Lindvall, C. & Umeton, R. Automated machine learning: Review of the state-of-the-art and alternatives for healthcare. Artif. Intell. Med. 104, 101822 (2020).Article
PubMed
Google Scholar
https://www.nature.com/articles/s41598-024-55874-0