Published on in Vol 21, No 4 (2019): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12437, first published .
A Stroke Risk Detection: Improving Hybrid Feature Selection Method

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

Journals

  1. Li X, Bian D, Yu J, Li M, Zhao D. Using machine learning models to improve stroke risk level classification methods of China national stroke screening. BMC Medical Informatics and Decision Making 2019;19(1) View
  2. Kasturiwale H, Kale S. Detection of Cardiac problems by the Extraction of Multimodal functions and Machine Learning techniques. IOP Conference Series: Materials Science and Engineering 2021;1022(1):012124 View
  3. Rathakrishnan K, Min S, Park S. Evaluation of ECG Features for the Classification of Post-Stroke Survivors with a Diagnostic Approach. Applied Sciences 2020;11(1):192 View
  4. Wu Y, Chen F, Song H, Feng W, Sun J, Liu R, Li D, Liu Y. Use of a Smartphone Platform to Help With Emergency Management of Acute Ischemic Stroke: Observational Study. JMIR mHealth and uHealth 2021;9(2):e25488 View
  5. Wang H, Avillach P. Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning. JMIR Medical Informatics 2021;9(4):e24754 View
  6. Allen A, Siefkas A, Pellegrini E, Burdick H, Barnes G, Calvert J, Mao Q, Das R. A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients. Applied Sciences 2021;11(12):5576 View
  7. Zanotto B, Beck da Silva Etges A, dal Bosco A, Cortes E, Ruschel R, De Souza A, Andrade C, Viegas F, Canuto S, Luiz W, Ouriques Martins S, Vieira R, Polanczyk C, André Gonçalves M. Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers. JMIR Medical Informatics 2021;9(11):e29120 View
  8. Abedi V, Razavi S, Khan A, Avula V, Tompe A, Poursoroush A, Vafaei Sadr A, Li J, Zand R. Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine. Journal of Clinical Medicine 2021;10(23):5710 View
  9. Chen M, Tan X, Padman R. A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study. Journal of Medical Internet Research 2023;25:e36477 View
  10. Pathan M, Nag A, Pathan M, Dev S. Analyzing the impact of feature selection on the accuracy of heart disease prediction. Healthcare Analytics 2022;2:100060 View
  11. Akyel A. Accurate estimation of stroke risk with fuzzy clustering and ensemble learning methods. Biomedical Signal Processing and Control 2022;77:103764 View
  12. Qiu Y, Cheng S, Wu Y, Yan W, Hu S, Chen Y, Xu Y, Chen X, Yang J, Chen X, Zheng H. Development of rapid and effective risk prediction models for stroke in the Chinese population: a cross-sectional study. BMJ Open 2023;13(3):e068045 View
  13. Miceli G, Basso M, Rizzo G, Pintus C, Cocciola E, Pennacchio A, Tuttolomondo A. Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review. Biomedicines 2023;11(4):1138 View
  14. Bathla P, Kumar R. A hybrid system to predict brain stroke using a combined feature selection and classifier. Intelligent Medicine 2023 View
  15. Pathan M, Jianbiao Z, John D, Nag A, Dev S. Identifying Stroke Indicators Using Rough Sets. IEEE Access 2020;8:210318 View

Books/Policy Documents

  1. Maheshwari H, Yadav D, Chandra U. Business Data Analytics. View
  2. Mallick S, Panda M. Innovations in Intelligent Computing and Communication. View
  3. Ait Temghart A, Marwan M, Baslam M. Computing, Internet of Things and Data Analytics. View