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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Using machine learning models to improve stroke risk level classification methods of China national stroke screening
Xuemeng Li, Di Bian, Jinghui Yu, et al.
BMC Medical Informatics and Decision Making (2019) Vol. 19, Iss. 1
Open Access | Times Cited: 47

Showing 26-50 of 47 citing articles:

Performance Enhancement of Machine Learning Algorithms on Heart Stroke Prediction Application using Sampling and Feature Selection Techniques
Naga Sreeharsha Reddy Ambati, Sree Harrsha Singara, Syam Sukesh Konjeti, et al.
2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (2022), pp. 488-495
Closed Access | Times Cited: 3
Using Machine Learning Models to Study Medication Adherence in Hypertensive Patients Based on National Stroke Screening Data
Xuemeng Li, Haifeng Xu, Mei Li, et al.
(2021), pp. 135-139
Closed Access | Times Cited: 4
Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm
Jenish Maharjan, Yasha Ektefaie, Logan Ryan, et al.
Frontiers in Neurology (2022) Vol. 12
Open Access | Times Cited: 3
Development of a gastric cancer risk calculator for questionnaire-based surveillance of Iranian dyspeptic patients
Kimiya Gohari, Samaneh Saberi, Maryam Esmaieli, et al.
BMC Gastroenterology (2024) Vol. 24, Iss. 1
Open Access
The Comparative Early Prediction Model for Cardiovascular Disease Using Machine Learning
Sri Sumarlinda, Azizah Rahmat, Zalizah binti Awang Long, et al.
International Journal of Scientific Research in Computer Science Engineering and Information Technology (2024), pp. 24-33
Open Access
Human Brain Stroke Prediction using Machine Learning Methods with Synthetic Minority Oversampling Approach
Muhammad Mujahid, Noor Ayesha, Ahmad Taher Azar, et al.
(2024), pp. 84-89
Closed Access
Exploring Feature Relationships in Brain Stroke Data Using Polynomial Feature Transformation and Linear Regression Modeling
Sitanaboina S L Parvathi, Aruna Devi B, Gururaj L. Kulkarni, et al.
Journal of Machine and Computing (2024), pp. 1158-1169
Open Access
SRPNet: stroke risk prediction based on two-level feature selection and deep fusion network
Daoliang Zhang, Na Yu, Xiaodan Yang, et al.
Frontiers in Physiology (2024) Vol. 15
Open Access
Application of Machine Learning Techniques for Predicting Stroke Disease
Muhammad Yasir Rafiq, A. Mohamed Nazeer, A Ezrati Gilani
VAWKUM Transactions on Computer Sciences (2024) Vol. 12, Iss. 2, pp. 123-136
Open Access
Harnessing the Power of Ensemble Machine Learning for the Heart Stroke Classification
Purnima Pal, Manju Nandal, Srishti Dikshit, et al.
EAI Endorsed Transactions on Pervasive Health and Technology (2023) Vol. 9
Open Access | Times Cited: 1
Content-based filtering using cosine similarity algorithm for alternative selection on training programs
Muhammad Falah Abdurrafi, Dewi Handayani Untari Ningsih
Journal of Soft Computing Exploration (2023) Vol. 4, Iss. 4, pp. 204-212
Open Access | Times Cited: 1
Return-to-Work Predictions for Chinese Patients With Occupational Upper Extremity Injury: A Prospective Cohort Study
Zhongfei Bai, Jiaqi Zhang, Chaozheng Tang, et al.
Frontiers in Medicine (2022) Vol. 9
Open Access | Times Cited: 2
Accurate estimation of stroke risk with fuzzy clustering and ensemble learning methods
Anıl Akyel
Biomedical Signal Processing and Control (2022) Vol. 77, pp. 103764-103764
Closed Access | Times Cited: 1
Towards a topic modeling approach to semi-automatically detect self-reported stroke symptoms (FAST symptoms) and their correlation with aphasia types
Emmanouil S. Rigas, Tatiana Pourliaka, Μαρία Παπουτσόγλου, et al.
Quality & Quantity (2022) Vol. 57, Iss. 2, pp. 1321-1336
Closed Access | Times Cited: 1
Artificial Intelligence and Machine Learning for Health Risks Prediction
Luke Oluwaseye Joel, Wesley Doorsamy, Babu Sena Paul
Studies in fuzziness and soft computing (2021), pp. 243-265
Closed Access | Times Cited: 1
Application of machine learning in measurement of ageing and geriatric diseases: A systematic review
Ayushi Das, Preeti Dhillon
Research Square (Research Square) (2023)
Open Access
Use of Artificial Intelligence in Cardiology: Where Are We in Africa?
Fatou Lo Niang, Vinasétan Ratheil Houndji, Moussa Lô, et al.
(2023), pp. 473-486
Closed Access
Stroke Risk Prediction Using Deep Neural Networks: Empowering Healthcare Services for Early Identification and Prevention
Rajvardhan Kurlekar, Arjun Sharma, S S Dalvi, et al.
(2023), pp. 1-7
Closed Access
Classification of Stroke Victims through Supervised Machine Learning Algorithms and Ensemble Learning
Dalton Hensley, Heba Elgazzar
2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (2022), pp. 0058-0064
Closed Access
Supervised Machine Learning System Based Segmentation and Classification of Strokes Using Deep Learning Techniques
V Ajay., S Akshatha, Metun Metun, et al.
2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) (2022) Vol. 9, pp. 1-5
Closed Access
Big Data Processing with Hadoop and Data Mining
Mukalad Faleh Hassan, Mehdi Ebady Manaa
2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (2022), pp. 1-8
Closed Access
Analysis and classification of main risk factors causing stroke in Shanxi Province
Junjie Liu, Yiyang Sun, Jing Ma, et al.
arXiv (Cornell University) (2021)
Open Access
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