The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier
Open accessAbstract
Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwatch and heart monitor device that is attached to the patient monitors the blood pressure and electrocardiogram (ECG). The MDCNN is utilized for classifying the received sensor data into normal and abnormal. The performance of the system is analyzed by comparing the proposed MDCNN with existing deep learning neural networks and logistic regression. The results demonstrate that the proposed MDCNN based heart disease prediction system performs better than other methods. The proposed method shows that for the maximum number of records, the MDCNN achieves an accuracy of 98.2 which is better than existing classifiers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,156
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Internet of Things (IoT): A vision, architectural elements, and future directions
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.01.010 · Cited by 12,293
Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
Mohan, S., Thirumalai, C., & Srivastava, G. (2019) · IEEE Access · doi:10.1109/access.2019.2923707 · Cited by 1,951
A comparative study of LPWAN technologies for large-scale IoT deployment
Mekki, K., Bajic, E., Chaxel, F., & Meyer, F. (2019) · ICT Express · doi:10.1016/j.icte.2017.12.005 · Cited by 1,547
Rahmani, A. M., Gia, T. N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., & Liljeberg, P. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2017.02.014 · Cited by 1,198
A Survey on Internet of Things and Cloud Computing for Healthcare
Dang, L. M., Piran, M. J., Han, D., Min, K., & Moon, H. (2019) · Electronics · doi:10.3390/electronics8070768 · Cited by 640
Haq, A. U., Li, J. P., Memon, M. H., Nazir, S., & Sun, R. (2018) · Mobile Information Systems · doi:10.1155/2018/3860146 · Cited by 621
RFID Technology for IoT-Based Personal Healthcare in Smart Spaces
Amendola, S., Lodato, R., Manzari, S., Occhiuzzi, C., & Marrocco, G. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2313981 · Cited by 608
Enabling technologies for fog computing in healthcare IoT systems
Mutlag, A. A., Abd Ghani, M. K., Arunkumar, N., Mohammed, M. A., & Mohd, O. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2018.07.049 · Cited by 590
Cited by
Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03612-z · Cited by 1,040
RetractedKhan, M. A., & Algarni, F. (2020) · IEEE Access · doi:10.1109/access.2020.3006424 · Cited by 283
Self-citationSmart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23020828 · Cited by 120
Yaqoob, M. M., Nazir, M., Khan, M. A., Qureshi, S., & Al-Rasheed, A. (2023) · Applied Sciences · doi:10.3390/app13031911 · Cited by 96
Self-citation
Metadata
- Authors
- Khan, M. A.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 34717-34727
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3007212997
- Cited by
- 346
- References
- 36
- FWCI
- 38.33
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier. https://app.cochat.ai/papers/10.1109/access.2020.2974687
- The paper
- Khan, M. A. (2020). An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier. IEEE Access. https://doi.org/10.1109/access.2020.2974687
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
