# A Preliminary Study on Machine Learning Techniques to Classify Cardiovascular Diseases in Mexico
## 1 Introduction  
Cardiovascular diseases (CVDs) are among the leading causes of mortality worldwide and can lead to severe cardiac complications, including heart disease [1]. In Mexico, CVDs accounted for 23.5% of total deaths in 2019, representing 156,041 fatalities out of the 88.8% of deaths attributed to non-communicable diseases [2]. This situation is even more critical in rural areas, where the lack of advanced medical equipment limits timely diagnosis and treatment, increasing the vulnerability of the population to these conditions [3]. The absence of medical equipment in Mexican rural clinics is mainly due to its high costs. However, this problem can be partially solved with the combination of two practical and low-cost tools, the first is the use of machine learning techniques and the second consists of using Arduino-based equipment [4, 5]. Previous research has applied machine learning to a variety of cardiovascular diseases, demonstrating its effectiveness in risk prediction and diagnosis. For instance, ML models have been used to predict coronary artery disease (CAD) by analyzing clinical parameters, such as cholesterol levels and electrocardiogram features [6]. Other studies have focused on heart failure prediction, utilizing patient demographics and echocardiographic data to assess the likelihood of disease progression [7]. Additionally, ML algorithms have been employed for arrhythmia classification, leveraging electrocardiographic signals to detect abnormalities in cardiac rhythms [8]. These applications highlight the versatility of ML in cardiovascular research and support its potential for developing low-cost, accessible diagnostic tools. Several studies have demonstrated that machine learning (ML) techniques can assist in classifying and predicting cardiovascular diseases with high accuracy. Algorithms such as logistic regression (LR), random forest (RF), and support vector machines (SVM) have been widely used in clinical applications to assess cardiovascular risk, providing reliable diagnoses without requiring costly and invasive testing (Table 1) [2; 3]. Additionally, the integration of ML models with low-cost hardware solutions, such as Arduino-based systems, has led to the development of portable and accessible medical diagnostic tools [4; 5]. The second tool, the Arduino board, has a wide range of applications [12], such as low-cost cameras to study animal behavior [13], greenhouse monitoring systems [14], and even systems for fluorescence detection [15], among other applications [16; 17]. Additionally, this board is low-cost, and the user does not need to have advanced knowledge in electronics and programming to use it. So, the Arduino board is a novel solution to design low-cost lab equipment, avoiding the high costs of lab equipment. The scientific community is using the Arduino Board to develop low-cost technologies, which have been implemented in different fields of study [18; 19]. The combination of the two previous tools (machine learning and Arduino) can be a preliminary proposal to the problem of the absence of medical equipment in rural clinics in Mexico. Therefore, this study aims to develop a machine learning-based cardiovascular risk classifier, prioritizing the use of non-invasive attributes (features) to facilitate its implementation in resource-limited settings. The model is trained and validated using the Heart Disease dataset from the UCI Machine Learning Repository, which includes the