Machine Learning Application for Predicting Heart Attacks in Patients from Europe

Even today, there are still a large number of people suffering from heart attacks, which have already claimed numerous lives worldwide. To examine the main components of this problem in an objective and timely manner, we chose to work with a methodology that relies on taking and learning from real and existing data for use in training and testing predictive models. This was carried out to obtain useful data for the present research work. There are in parallel different methodologies that do not quite fit the model of this work. Data was collected from the "Center for Machine Learning and Intelligent Systems" which in turn contains data from patients who have ever suffered a cardiovascular attack and from patients who never suffered the disease, all of them being patients selected from different medical institutions. With the corresponding information, it was subjected to different processes such as cleaning, preparation, and training with the data, to obtain a logistic regression type automatic learning model ready to predict whether or not a person may suffer a cardiovascular attack. Finally, a result of 87% accuracy was obtained for people who suffered a heart attack and an accuracy of 81% for people who would not suffer from this disease. This can greatly reduce the mortality rate due to infarction, by knowing the condition of a person who is unaware of his or her health situation and thus being able to take appropriate measures.

@article{Elescano-Avendaño2022,
  title     = {Machine Learning Application for Predicting Heart Attacks in Patients from Europe},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {2},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Enrique Arturo Elescano-Avendaño and Freddy Edson Huamán-Leon and Gilson Andreson Vasquez-Torres and Dayana Ysla-Espinoza and Enrique Lee Huamaní and Alexi Delgado},
  doi       = {10.14569/IJACSA.2022.0130240},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130240}
}