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Heart disease is one of the diseases known to have highest mortality rate, with estimated that one in third of death per year is caused by heart disease. Heart disease comes randomly and can’t be predicted on whether a patient has a heart attack. Potential of heart attack can be predicted with analysing the health situation of the patient. This research is used to predict the heart disease potential in a patient. This research uses statistical methods, namely Logistic Regression and uses deep learning, namely Neural Network. Comparison between the two models is expected to produce the best model for predicting heart disease. In this experiment the neural network has a very high accuracy reaching from 80% to 94% with minimal loss and based on the curve of the model it does not seems to have an overfitting.

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Heart Attack Prediction Using Deep Learning Methods

  • 会议论文

  • 发表日期：

    2026年

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  • Ivo Herid Lesmana,
  • Syarifah Diana Permai,
  • Jeklin Harefa,
  • Gredion Prajena

摘要

Heart disease is one of the diseases known to have highest mortality rate, with estimated that one in third of death per year is caused by heart disease. Heart disease comes randomly and can’t be predicted on whether a patient has a heart attack. Potential of heart attack can be predicted with analysing the health situation of the patient. This research is used to predict the heart disease potential in a patient. This research uses statistical methods, namely Logistic Regression and uses deep learning, namely Neural Network. Comparison between the two models is expected to produce the best model for predicting heart disease. In this experiment the neural network has a very high accuracy reaching from 80% to 94% with minimal loss and based on the curve of the model it does not seems to have an overfitting.