Apply machine learning to predict cardiovascular risk in rural clinics from Mexico / Misael Zambrano-de la Torre ... [et al.]

Approximately 41 million people in the world die each year from cardiovascular diseases. In Mexico, it is one of the main causes of death per year. This problem is even more critical in rural areas of Mexico. Due to the limited number of specialized medical equipment available in these
clinics. Therefore, the objective of this work is to propose a new stage in the methodology used in machine learning for the classification of cardiovascular risk in rural clinics in Mexico. The importance of this work is being able to classify patients based only on non-invasive attributes,
avoiding the use of specialized clinical equipment. For this purpose, the Heart Disease Data Set repository is used to implement the new stage. The methodology to be implemented consists of 6 stages. The performance of the three algorithms is compared in terms of four parameters. The results
obtained show that only 4 attributes are required for classification with an 80% acceptance rate.