Authors :
Athira V. P.
Volume/Issue :
Volume 11 - 2026, Issue 3 - March
Google Scholar :
https://tinyurl.com/87nbja7h
Scribd :
https://tinyurl.com/3txvvj9c
DOI :
https://doi.org/10.38124/ijisrt/26mar713
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Heart attack is one of the leading causes of death worldwide. Early detection and prediction can significantly
reduce mortality by enabling timely medical intervention. In recent years, image processing and machine learning
techniques have been widely applied in the healthcare sector for disease diagnosis and prediction. In this research, image
processing techniques are combined with machine learning algorithms to predict the possibility of heart attack using
medical images and patient data. Medical imaging data such as ECG graphs or cardiac images are processed using feature
extraction techniques. These features are then classified using machine learning algorithms such as Random Forest,
Decision Tree, Naïve Bayes, and Support Vector Machine (SVM). Experimental results show that the Random Forest
classifier achieved the highest prediction accuracy compared with other algorithms. The proposed system demonstrates
that image processing combined with machine learning can support doctors in early diagnosis and decision-making.
Keywords :
Heart Attack Prediction, Image Processing, Machine Learning, WEKA, Classification, Feature Extraction.
References :
- G. Eason, B. Noble, and I.N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529-551, April 1955.
- J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892.
- I.S. Jacobs and C.P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, Academic Press, 1963.
- K. Elissa, “Heart disease prediction using machine learning techniques,” unpublished.
5. R. Nicole, “Machine learning models for medical image analysis,” Journal of Medical Informatics, in press.
Heart attack is one of the leading causes of death worldwide. Early detection and prediction can significantly
reduce mortality by enabling timely medical intervention. In recent years, image processing and machine learning
techniques have been widely applied in the healthcare sector for disease diagnosis and prediction. In this research, image
processing techniques are combined with machine learning algorithms to predict the possibility of heart attack using
medical images and patient data. Medical imaging data such as ECG graphs or cardiac images are processed using feature
extraction techniques. These features are then classified using machine learning algorithms such as Random Forest,
Decision Tree, Naïve Bayes, and Support Vector Machine (SVM). Experimental results show that the Random Forest
classifier achieved the highest prediction accuracy compared with other algorithms. The proposed system demonstrates
that image processing combined with machine learning can support doctors in early diagnosis and decision-making.
Keywords :
Heart Attack Prediction, Image Processing, Machine Learning, WEKA, Classification, Feature Extraction.
