# A Preliminary Study on Machine Learning Techniques to Classify Cardiovascular Diseases in Mexico
## 2 Materials and Methods  
First, the methodology used for patient data classification is presented. Additionally, one new stage has been proposed to assign each attribute as \"invasive\" and \"non-invasive\". The proposed new stage has two characteristics: (1) classification between invasive and non-invasive attributes, and (2) reduction of diagnosis time. Also, this section describes each stage in detail. In the first subsection some preliminaries of LR, RF, and SVM, are presented, then in a second subsection the low-cost electronic device Arduino-based components are described, which are required to implement the retained algorithms. Also, the heart rate sensor used in this study is the MAX30100, a pulse oximeter and heart rate monitoring sensor manufactured by Maxim Integrated (San Jose, CA, USA). The sensor is designed for the non-invasive measurement of heart rate and blood oxygen saturation (SpO\({}_{2}\)) levels by utilizing a combination of infrared (IR) and red light-emitting diodes (LEDs) along with a photodetector to measure changes in blood volume. The MAX30100 has been widely used in wearable medical devices due to its low power consumption and compact design, making it suitable for integration into portable health monitoring systems. The sensor was integrated into the Buzzduino-001 classifier, and its measurements were validated against a commercial pulse oximeter to ensure accuracy. In the third subsection, the characteristics of the patients \"Experimental data\" is described. Finally, the Buzzduino-001 cardiovascular risk classifier is presented, and its operation procedure is described in detail. The machine learning algorithms are now analyzed. Normally, to process patient cardiovascular health information, five stages are used, according to [PERSON] et al. (20), but these stages do not distinguish between invasive and non-invasive attributes. For this reason, in this work it is proposed to add an additional step in the methodology normally used to classify information. This new stage considers a classification between invasive and non-invasive attributes (features). An advantage of this proposed stage is to quickly know the diagnosis of a patient. Using the new proposed stage, it was possible to classify between a \"high\" or \"low\" risk of cardiovascular disease. Figure 1 shows the methodology used to classify cardiovascular risk, and to determine the risk of cardiovascular disease. Each stage is described as follows:Stage 1: Data Collection was performed using the database \"The Heart Disease Data Set\", it comes from the UCI Machine Learning Repository [21]. This database is available for free use and has 14 attributes (features or characteristics) and 303 patients (observations). It consists of multiple clinical and diagnostic attributes, including patient demographics, vital signs, and test results. The dataset includes both numerical and categorical variables, which are essential for model training and feature selection. The primary objective of this dataset is to predict the presence of heart disease using non-invasive clinical measurements. The target variable, \"target\", indicates whether a patient has heart disease (1) or not (0). This database allows for training and testing the proposed algorithm to predict cardiovascular disease risk. In Stage 2, preprocessing consists of identifying errors (outliers and missing values) in each attribute and observation. Once the data had been preprocessed, a database with 14 attributes and 298 observations was obtained. Finally, a univariate analysis was performed to determine the type of data distribution and its correlation. At Stage 3, the \"non-invasive\" feature detection and indiscriminate selection of attributes is carried out. This affects the diagnosis generated by the machine learning algorithm, leading to wasted time and financial resources. To address this, an additional step is introduced in the existing methodology.