Heart Patient Scanning, Visualization, and Monitoring Using Machine Learning
Abstract
According to the World Health Organization, cardiovascular or total heart-related disorders cause 17.9 million deaths annually, making them the top cause of mortality worldwide. Early illness identification and detection are crucial, because they may hold the key to a cure. The main problem is identifying diseases at an early stage, hence most scientists and studies concentrate on machine learning algorithms that can accurately recognise vast amounts of complicated data. These approaches are then used to support healthcare. By using several Machine Learning Algorithms, such as KNN Decision Tree (DT), Logistic Regression, SVM, Random Forest (RF), and others, this effort aims to identify cardiac illnesses at an early stage and prevent repercussions.
References
Amir Hussain, Peipei Yang, Mufti Mahmud, Jan Karasek et al., A Novel Cardiovascular Decision Support Framework foreffective clinical Risk Assessment, IEEE.
Puneet Bansal, Ridhi Saini et al., "Classification of heart diseases from ECG signals using wavelet transform and kNN classifier", International Conference on Computing Communication and Automation (ICCCA2015).
Jiwani, N., Gupta, K., Sharif, M. H. U., Adhikari, N., & Afreen, N. (2022, October). A LSTM-CNN Model for Epileptic Seizures Detection using EEG Signal. In 2022 2nd International Conference on Emerging Smart Technologies and Applications (eSmarTA) (pp. 1-5). IEEE.
K. Vembandasamy, R. Sasipriya and E. Deepa, "Heart Diseases Detection Using Naive Bayes Algorithm", IJISET-International Journal of Innovative Science Engineering & Technology, vol. 2, pp. 441-444, 2015.
Seyedamin Pouriyeh, Sara Vahid, Giovanna Sannino, Giuseppe De Pietro, Hamid Arabnia, Juan Gutierrez et al., A Comprehensive Investigation and.
Jiwani, N., Gupta, K., & Whig, P. (2023). Assessing Permeability Prediction of BBB in the Central Nervous System Using ML. In International Conference on Innovative Computing and Communications (pp. 449-459). Springer, Singapore.
Gupta, K., Jiwani, N., & Whig, P. (2023). Effectiveness of Machine Learning in Detecting Early-Stage Leukemia. In International Conference on Innovative Computing and Communications (pp. 461-472). Springer, Singapore.
D. Krishnani, A. Kumari, A. Dewangan, A. Singh and N.S. Naik, "Prediction of Coronary Heart Disease using Supervised Machine Learning Algorithms", TENCON 2019 — 2019 IEEE Reg. 10 Conf., pp. 367-372, 2019.
Jiwani, N., Gupta, K., & Whig, P. (2023). Analysis of the Potential Impact of Omicron Crises Using NLTK (Natural Language Toolkit). In Proceedings of Third Doctoral Symposium on Computational Intelligence (pp. 445-454). Springer, Singapore.