PMID- 38431729
OWN - NLM
STAT- MEDLINE
DCOM- 20240305
LR  - 20240306
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 14
IP  - 1
DP  - 2024 Mar 2
TI  - Migraine headache (MH) classification using machine learning methods with data 
      augmentation.
PG  - 5180
LID - 10.1038/s41598-024-55874-0 [doi]
LID - 5180
AB  - Migraine headache, a prevalent and intricate neurovascular disease, presents 
      significant challenges in its clinical identification. Existing techniques that 
      use subjective pain intensity measures are insufficiently accurate to make a 
      reliable diagnosis. Even though headaches are a common condition with poor 
      diagnostic specificity, they have a significant negative influence on the brain, 
      body, and general human function. In this era of deeply intertwined health and 
      technology, machine learning (ML) has emerged as a crucial force in transforming 
      every aspect of healthcare, utilizing advanced facilities ML has shown 
      groundbreaking achievements related to developing classification and automatic 
      predictors. With this, deep learning models, in particular, have proven effective 
      in solving complex problems spanning computer vision and data analytics. 
      Consequently, the integration of ML in healthcare has become vital, especially in 
      developing countries where limited medical resources and lack of awareness 
      prevail, the urgent need to forecast and categorize migraines using artificial 
      intelligence (AI) becomes even more crucial. By training these models on a 
      publicly available dataset, with and without data augmentation. This study 
      focuses on leveraging state-of-the-art ML algorithms, including support vector 
      machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree 
      (DST), and deep neural networks (DNN), to predict and classify various types of 
      migraines. The proposed models with data augmentations were trained to classify 
      seven various types of migraine. The proposed models with data augmentations were 
      trained to classify seven various types of migraine. The revealed results show 
      that DNN, SVM, KNN, DST, and RF achieved an accuracy of 99.66%, 94.60%, 97.10%, 
      88.20%, and 98.50% respectively with data augmentation highlighting the 
      transformative potential of AI in enhancing migraine diagnosis.
CI  - (c) 2024. The Author(s).
FAU - Khan, Lal
AU  - Khan L
AD  - Department of Computer Science, Ibadat International University Islamabad 
      Pakpattan Campus, Pakpattan, Pakistan.
FAU - Shahreen, Moudasra
AU  - Shahreen M
AD  - Department of Computer Science, Mir Chakar Khan Rind University, Sibi, Pakistan.
FAU - Qazi, Atika
AU  - Qazi A
AD  - Centre for Lifelong Learning, Universiti Brunei Darussalam, Bandar Seri Begawan, 
      Brunei Darussalam.
FAU - Jamil Ahmed Shah, Syed
AU  - Jamil Ahmed Shah S
AD  - Department of Computer Science, Mir Chakar Khan Rind University, Sibi, Pakistan.
FAU - Hussain, Sabir
AU  - Hussain S
AD  - Department of Agriculture, Mir Chakar Khan Rind University, Sibi, Pakistan.
FAU - Chang, Hsien-Tsung
AU  - Chang HT
AD  - Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, 
      Taiwan. smallpig@cgu.edu.tw.
AD  - Department of Computer Science and Information Engineering, Chang Gung 
      University, Taoyuan, Taiwan. smallpig@cgu.edu.tw.
AD  - Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, 
      Taoyuan, Taiwan. smallpig@cgu.edu.tw.
LA  - eng
GR  - 112-2410-H-182-026-MY2/National Science and Technology Council,Taiwan/
GR  - NERPD4N0231/Chang Gung Memorial Hospital, Linkou/
PT  - Journal Article
DEP - 20240302
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Humans
MH  - *Artificial Intelligence
MH  - Machine Learning
MH  - Neural Networks, Computer
MH  - Algorithms
MH  - *Migraine Disorders/diagnosis
MH  - Support Vector Machine
PMC - PMC10908834
COIS- The authors declare no competing interests.
EDAT- 2024/03/03 17:42
MHDA- 2024/03/05 06:44
PMCR- 2024/03/02
CRDT- 2024/03/02 23:28
PHST- 2023/05/26 00:00 [received]
PHST- 2024/02/28 00:00 [accepted]
PHST- 2024/03/05 06:44 [medline]
PHST- 2024/03/03 17:42 [pubmed]
PHST- 2024/03/02 23:28 [entrez]
PHST- 2024/03/02 00:00 [pmc-release]
AID - 10.1038/s41598-024-55874-0 [pii]
AID - 55874 [pii]
AID - 10.1038/s41598-024-55874-0 [doi]
PST - epublish
SO  - Sci Rep. 2024 Mar 2;14(1):5180. doi: 10.1038/s41598-024-55874-0.