Authors :
Shubhranvi Kapare; Roshani Gaikwad; Manisha Kalokhe
Volume/Issue :
Volume 10 - 2025, Issue 1 - January
Google Scholar :
https://tinyurl.com/5bu3t33w
Scribd :
https://tinyurl.com/k2e45xja
DOI :
https://doi.org/10.5281/zenodo.14885971
Abstract :
20.5 million people died of Cardiovascular diseases(CVDs) in 2021, making it the most common cause of deaths.
Four out of five of these deaths were in low and middle income countries. It is said that over 80% of these deaths could have
been prevented by early intervention. The reason why high income countries have lower death rates is because they are able
to invest more in their health care system thus increasing the rate of early intervention. But in the USA, one third of the
deaths are still caused by CVDs. This is why early an increase in accuracy of identifying CVDs and a decrease in the resources
needed is necessary. This study reviews a few recent papers which are connected to CVDs.
References :
- Detecting and Classifying Myocardial Infarction in Echocardiogram Frames With an Enhanced CNN Algorithm and ECV-3D Network
- T. Ullah et al., "Machine Learning-Based Cardiovascular Disease Detection Using Optimal Feature Selection," in IEEE Access, vol. 12, pp. 16431-16446, 2024, doi: 10.1109/ACCESS.2024.3359910
- Enhanced Myocardial Infarction Identification in Phonocardiogram Signals Using Segmented Feature Extraction and Transfer Learning-Based Classification
- N. A. Vinay, K. N. Vidyasagar, S. Rohith, D. Pruthviraja, S. Supreeth and S. H. Bharathi, "An RNN-Bi LSTM Based Multi Decision GAN Approach for the Recognition of Cardiovascular Disease (CVD) From HeartBeat Sound: A Feature Optimization Process," in IEEE Access, vol. 12, pp. 65482-65502, 2024, doi: 10.1109/ACCESS.2024.3397574.
- T. Sinha Roy, J. K. Roy and N. Mandal, "Conv-Random Forest-Based IoT: A Deep Learning Model Based on CNN and Random Forest for Classification and Analysis of Valvular Heart Diseases," in IEEE Open Journal of Instrumentation and Measurement, vol. 2, pp. 1-17, 2023, Art no. 2500717, doi: 10.1109/OJIM.2023.3320765.
- C. Zou et al., "DWT-CNNTRN: a Convolutional Transformer for ECG Classification with Discrete Wavelet Transform," 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney,Australia, 2023, pp. 1-6, doi: 10.1109/EMBC40787.2023.10340561.
- D. Y. Omkari and K. Shaik, "An Integrated Two-Layered Voting (TLV) Framework for Coronary Artery Disease Prediction Using Machine Learning Classifiers," in IEEE Access, vol. 12, pp. 56275-56290, 2024, doi: 10.1109/ACCESS.2024.3389707.
20.5 million people died of Cardiovascular diseases(CVDs) in 2021, making it the most common cause of deaths.
Four out of five of these deaths were in low and middle income countries. It is said that over 80% of these deaths could have
been prevented by early intervention. The reason why high income countries have lower death rates is because they are able
to invest more in their health care system thus increasing the rate of early intervention. But in the USA, one third of the
deaths are still caused by CVDs. This is why early an increase in accuracy of identifying CVDs and a decrease in the resources
needed is necessary. This study reviews a few recent papers which are connected to CVDs.