Early Cardiac Disease Detection Using Neural Networks

Calderon-Vilca, Hugo D.; Callupe, Kevin E. Chagua; Aliaga, Richard J. Inga; Cuba, Jair Barzola; Marino-Cardenas, Flor C.

doi:10.1109/iestec46403.2019.00106

Cited by 8 publications

(1 citation statement)

References 12 publications

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“…As a consequence of the complexity and unpredictability of these sectors, smart structures, such as the fuzzy system and artificial neural network, as well as genetic algorithms, have been created. Smoking, cholesterol, blood pressure, diabetes, sex, and age are the key risk factors for ischemic heart disease [ 16 ]. Heart disease is difficult to identify because of the numerous unknown risk factors [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques

Shehzadi

Hassan

Rizwan

et al. 2022

Computational Intelligence and Neuroscience

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Ischemic heart disease (IHD) causes discomfort or irritation in the chest. According to the World Health Organization, coronary heart disease is the major cause of mortality in Pakistan. Accurate model with the highest precision is necessary to avoid fatalities. Previously several models are tried with different attributes to enhance the detection accuracy but failed to do so. In this research study, an artificial approach to categorize the current stage of heart disease is carried out. Our model predicts a precise diagnosis of chronic diseases. The system is trained using a training dataset and then tested using a test dataset. Machine learning methods such as LR, NB, and RF are applied to forecast the development of a disease. Experimental outcomes of this research study have proven that our strategy has excelled other procedures with maximum accuracy of 99 percent for RF, 97 percent for NB, and 98 percent for LR. With such high accuracy, the number of deaths per year of ischemic heart disease will be slightly decreased.

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Section: Introductionmentioning

confidence: 99%