A SUPERVISED LEARNING-BASED APPROACH FOR PREDICTING CARDIOMYOPATHY DISEASE IN HEART PATIENTS’ CARDIOVASCULAR HEALTH PREDICTION
Keywords:
Random Forest, ETC, Classification, CardiomyopathyAbstract
Cardiomyopathy is a chronic and often progressive heart condition characterized by abnormalities in the structure or function of the heart muscle. Early detection of cardiomyopathy is essential for effective management and treatment, as it can lead to life-threatening complications such as heart failure, arrhythmias, and sudden cardiac death. Conventional diagnostic methods primarily rely on clinical assessments, electrocardiograms (ECGs), and echocardiography, which may not always provide accurate predictions or early warnings. Moreover, these approaches tend to overlook the potential influence of genetic and lifestyle factors, which are increasingly recognized as critical contributors to cardiomyopathy risk. The conventional diagnostic system for cardiomyopathy suffers from several limitations. Clinical assessments, while valuable, often rely on subjective judgments and may not detect subtle changes in heart function until the disease has progressed significantly. ECGs and echocardiography can be more objective but may miss early-stage cardiomyopathy. Furthermore,these approaches typically do not consider genetic predispositions or lifestyle factors, which can significantly affect disease risk.
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References
Bakar, Wan Aezwani Wan Abu, et al. "A Review: Heart Disease Prediction in Machine
Learning & Deep Learning." 2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA). IEEE, 2023.
Dileep, P., et al. "An automatic heart disease prediction using cluster-based bi-directional
LSTM (C-BiLSTM) algorithm." Neural Computing and Applications 35.10 (2023): 7253-7266.
Mishra, Nilamadhab, et al. "Visual Analysis of Cardiac Arrest Prediction Using Machine
Learning Algorithms: A Health Education Awareness Initiative." Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines. IGI Global, 2023.
-363.
Guo, Saidi, et al. "Survival prediction of heart failure patients using motion-based analysis
method." Computer Methods and Programs in Biomedicine 236 (2023): 107547.
Nandy, Sudarshan, et al. "An intelligent heart disease prediction system based on swarm-
artificial neural network." Neural Computing and Applications 35.20 (2023): 14723-14737.
Pant, Aman, et al. "Heart disease prediction using image segmentation Through the CNN
model." 2023 13th International Conference on Cloud Computing, Data Science &
Engineering (Confluence). IEEE, 2023.
Nandy, Sudarshan, et al. "An intelligent heart disease prediction system based on swarm-
artificial neural network." Neural Computing and Applications 35.20 (2023): 14723-14737.
Rani, P., Kumar, R., Ahmed, N.M.O.S. et al. A decision support system for heart disease
prediction based upon machine learning. J Reliable Intell Environ 7, 263–275 (2021).
ttps://doi.org/10.1007/s40860-021-00133-6
M. Kavitha, G. Gnaneswar, R. Dinesh, Y. R. Sai and R. S. Suraj, "Heart Disease Prediction
using Hybrid machine Learning Model," 2021 6th International Conference on Inventive Computation Technologies (ICICT), 2021, pp. 1329-1333, doi:
1109/ICICT50816.2021.9358597.
S. Mohan, C. Thirumalai and G. Srivastava, "Effective Heart Disease Prediction
Using Hybrid Machine Learning Techniques," in IEEE Access, vol. 7, pp. 81542-81554,
, doi: 10.1109/ACCESS.2019.2923707.
Shah, D., Patel, S. & Bharti, S.K. Heart Disease Prediction using Machine Learning
Techniques. SN COMPUT. SCI. 1, 345 (2020). https://doi.org/10.1007/s42979-020-00365-y
C. Guo, J. Zhang, Y. Liu, Y. Xie, Z. Han and J. Yu, "Recursion Enhanced Random Forest
with an Improved Linear Model (RERF-ILM) for Heart Disease Detection on the Internet of
Medical Things Platform," in IEEE Access, vol. 8, pp. 59247-59256, 2020, doi:
1109/ACCESS.2020.2981159.
Hager Ahmed, Eman M.G. Younis, Abdeltawab Hendawi, Abdelmgeid A. Ali, Heart
disease identification from patients’ social posts, machine learning solution on Spark, Future
Generation Computer Systems, Volume 111, 2020, Pages 714-722, ISSN 0167-739X,
https://doi.org/10.1016/j.future.2019.09.056.
Katarya, R., Meena, S.K. Machine Learning Techniques for Heart Disease Prediction:
A Comparative Study and Analysis. Health Technol. 11, 87–97 (2021).
https://doi.org/10.1007/s12553-020-00505-7
Kannan, R., Vasanthi, V. (2019). Machine Learning Algorithms with ROC Curve for
Predicting and Diagnosing the heart disease. In: Soft Computing and Medical Bioinformatics.
SpringerBriefs in Applied Sciences and Technology (). Springer, Singapore.
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