Apply machine learning to predict cardiovascular risk in rural clinics from Mexico

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.Aproximadamente 41 millones de personas en el mundo mueren cada año por enfermedades cardiovasculares. En México es una de las principales causas de muerte al año. Este problema es aún más crítico en las zonas rurales de México. Debido al número limitado de equipo médico especializado disponible en estas clínicas. Por tanto, el objetivo de este trabajo es proponer una nueva etapa en la metodología utilizada en aprendizaje automático para la clasificación del riesgo cardiovascular en clínicas rurales de México. La importancia de este trabajo es poder clasificar a los pacientes en base únicamente a atributos no invasivos, evitando el uso de equipos clínicos especializados. Para ello, se utiliza el repositorio Heart Disease Data Set para implementar la nueva etap

Prediction of biochemical oxygen demand in mexican surface waters using machine learning

The monitoring of surface water quality is insufficient in Mexico due to the limited water monitoring stations. The main monitoring parameter to evaluate surface water quality is the biochemical oxygen demand. This parameter estimates the biodegradable organic matter present in the water. Concentrations above 30 mg/l indicates a high level of contamination by domestic and industrial waste. Therefore, the aim of this work to provide a reference to the conventional process of determining biochemical oxygen demand using machine learning. The database used was collected by the National Water Commission (CONAGUA). Pearson’s correlation and Forward Selection techniques were applied to identify the parameters with the most important contribution to prediction of biochemical oxygen demand. Two groups were formed and used as input to four machine learning algorithms. Random forest algorithm obtained the best performance. Group 1 and 2 of parameters obtained a 0.76 and 0.75 coefficient of determination respectively. This allows choosing an adequate group of parameters that can be determined with the chemical analysis instruments available in the study area