IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0285455.html
   My bibliography  Save this article

Henry gas solubility optimization double machine learning classifier for neurosurgical patients

Author

Listed:
  • Diana T Mosa
  • Amena Mahmoud
  • John Zaki
  • Shaymaa E Sorour
  • Shaker El-Sappagh
  • Tamer Abuhmed

Abstract

This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several algorithms are utilized such as k-nearest neighbor, Random Forest (RF), C4.5, Artificial Neural Network, and Support Vector Machine (SVM). Their performance is assessed using anonymous patients’ data. Then, a proposed double classifier based on Henry Gas Solubility Optimization (HGSO) is developed with Aquila optimizer (AQO). It is implemented for feature selection to classify patients’ outcome status into four states. Those are mortality, morbidity, improved, or the same. The double classifiers are evaluated via various performance metrics including recall, precision, F-measure, accuracy, and sensitivity. Another contribution of this research is the original use of hybrid technique based on RF-SVM and HGSO to predict patient outcome status with high accuracy. It determines outcome status relationship with age and mode of trauma. The algorithm is tested on more than 1000 anonymous patients’ data taken from a Neurosurgical unit of Mansoura International Hospital, Egypt. Experimental results show that the proposed method has the highest accuracy of 99.2% (with population size = 30) compared with other classifiers.

Suggested Citation

  • Diana T Mosa & Amena Mahmoud & John Zaki & Shaymaa E Sorour & Shaker El-Sappagh & Tamer Abuhmed, 2023. "Henry gas solubility optimization double machine learning classifier for neurosurgical patients," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-22, May.
  • Handle: RePEc:plo:pone00:0285455
    DOI: 10.1371/journal.pone.0285455
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0285455
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0285455&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0285455?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hamed Asadi & Richard Dowling & Bernard Yan & Peter Mitchell, 2014. "Machine Learning for Outcome Prediction of Acute Ischemic Stroke Post Intra-Arterial Therapy," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wenjuan Wang & Martin Kiik & Niels Peek & Vasa Curcin & Iain J Marshall & Anthony G Rudd & Yanzhong Wang & Abdel Douiri & Charles D Wolfe & Benjamin Bray, 2020. "A systematic review of machine learning models for predicting outcomes of stroke with structured data," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    2. Yizhao Ni & Kathleen Alwell & Charles J Moomaw & Daniel Woo & Opeolu Adeoye & Matthew L Flaherty & Simona Ferioli & Jason Mackey & Felipe De Los Rios La Rosa & Sharyl Martini & Pooja Khatri & Dawn Kle, 2018. "Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-20, February.
    3. Josline Adhiambo Otieno & Jenny Häggström & David Darehed & Marie Eriksson, 2024. "Developing machine learning models to predict multi-class functional outcomes and death three months after stroke in Sweden," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-16, May.
    4. Yao Tong & Beilei Lin & Gang Chen & Zhenxiang Zhang, 2022. "Predicting Continuity of Asthma Care Using a Machine Learning Model: Retrospective Cohort Study," IJERPH, MDPI, vol. 19(3), pages 1-18, January.
    5. Esra Zihni & Vince Istvan Madai & Michelle Livne & Ivana Galinovic & Ahmed A Khalil & Jochen B Fiebach & Dietmar Frey, 2020. "Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-15, April.
    6. Wieslaw L Nowinski & Varsha Gupta & Guoyu Qian & Wojciech Ambrosius & Radoslaw Kazmierski, 2014. "Population-Based Stroke Atlas for Outcome Prediction: Method and Preliminary Results for Ischemic Stroke from CT," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-11, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0285455. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.