Abstract
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.
Highlights
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
The problem shows the frequency of cardiac problems in different groups of people, being the main problem the heart attack, for this problem we propose the decision making of the machine learning methodology logistic regression
The present research work collected accurate information from medical institutions on patients who have ever suffered heart attack problems and on patients who have never suffered such disease, imported libraries for data preparation, data cleaning, and the development of the machine learning model, which in the present case was of the logistic regression type, which gives a result of 1 when there is a presence of probability or 0 when there is an absence of probability
Summary
Nowadays it is more common to talk about people prone to cardiac arrest [1], the lifestyle of the general population has changed so drastically that people have started to develop cardiovascular problems frequently [2]. To analyze the main factors of this problem in an objective and timely manner, we chose to work with a meta-analysis methodology [4], This consists of taking and studying existing test data and sorting them to obtain data beneficial to our research [5]. As the main study sample in this work, we took data from various medical institutions in Europe to compare and analyze why and how cardiovascular diseases have been growing. We took data from the "Center for Machine Learning and Intelligent Systems" and acquired a CSV with the corresponding information [7], by doing this, we were able to structure the information to obtain statistical tables that help to understand the problem [8]
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