A machine learning approach to personalized predictors of dyslipidemia: a cohort study

IntroductionMexico ranks second in the global prevalence of obesity in the adult population, which increases the probability of developing dyslipidemia. Dyslipidemia is closely related to cardiovascular diseases, which are the leading cause of death in the country. Therefore, developing tools that facilitate the prediction of dyslipidemias is essential for prevention and early treatment.MethodsIn this study, we utilized a dataset from a Mexico City cohort consisting of 2,621 participants, men and women aged between 20 and 50 years, with and without some type of dyslipidemia. Our primary objective was to identify potential factors associated with different types of dyslipidemia in both men and women. Machine learning algorithms were employed to achieve this goal. To facilitate feature selection, we applied the Variable Importance Measures (VIM) of Random Forest (RF), XGBoost, and Gradient Boosting Machine (GBM). Additionally, to address class imbalance, we employed Synthetic Minority Over-sampling Technique (SMOTE) for dataset resampling. The dataset encompassed anthropometric measurements, biochemical tests, dietary intake, family health history, and other health parameters, including smoking habits, alcohol consumption, quality of sleep, and physical activity.ResultsOur results revealed that the VIM algorithm of RF yielded the most optimal subset of attributes, closely followed by GBM, achieving a balanced accuracy of up to 80%. The selection of the best subset of attributes was based on the comparative performance of classifiers, evaluated through balanced accuracy, sensitivity, and specificity metrics.DiscussionThe top five features contributing to an increased risk of various types of dyslipidemia were identified through the machine learning technique. These features include body mass index, elevated uric acid levels, age, sleep disorders, and anxiety. The findings of this study shed light on significant factors that play a role in dyslipidemia development, aiding in the early identification, prevention, and treatment of this condition

The Latin American experience of allografting patients with severe aplastic anaemia: real-world data on the impact of stem cell source and ATG administration in HLA-identical sibling transplants

We studied 298 patients with severe aplastic anaemia (SAA) allografted in four Latin American countries. The source of cells was bone marrow (BM) in 94 patients and PBSCs in 204 patients. Engraftment failed in 8.1% of recipients with no difference between BM and PBSCs (P = 0.08). Incidence of acute GvHD (aGvHD) for BM and PBSCs was 30% vs 32% (P = 0.18), and for grades III–IV was 2.6% vs 11.6% (P = 0.01). Chronic GvHD (cGvHD) between BM and PBSCs was 37% vs 59% (P = 0.002) and extensive 5% vs 23.6% (P = 0.01). OS was 74% vs 76% for BM vs PBSCs (P = 0.95). Event-free survival was superior in patients conditioned with anti-thymocyte globulin (ATG)-based regimens compared with other regimens (79% vs 61%, P = 0.001) as excessive secondary graft failure was seen with other regimens (10% vs 26%, P = 0.005) respectively. In multivariate analysis, aGvHD II–IV (hazard ratio (HR) 2.50, confidence interval (CI) 1.1–5.6, P = 0.02) and aGvHD III–IV (HR 8.3 CI 3.4–20.2, Po0.001) proved to be independent negative predictors of survival. In conclusion, BM as a source of cells and ATG-based regimens should be standard because of higher GvHD incidence with PBSCs, although the latter combining with ATG in the conditioning regimen could be an option in selected high-risk patient

Classification of Cyber-Aggression Cases Applying Machine Learning

The adoption of electronic social networks as an essential way of communication has become one of the most dangerous methods to hurt people’s feelings. The Internet and the proliferation of this kind of virtual community have caused severe negative consequences to the welfare of society, creating a social problem identified as cyber-aggression, or in some cases called cyber-bullying. This paper presents research to classify situations of cyber-aggression on social networks, specifically for Spanish-language users of Mexico. We applied Random Forest, Variable Importance Measures (VIMs), and OneR to support the classification of offensive comments in three particular cases of cyber-aggression: racism, violence based on sexual orientation, and violence against women. Experimental results with OneR improve the comment classification process of the three cyber-aggression cases, with more than 90% accuracy. The accurate classification of cyber-aggression comments can help to take measures to diminish this phenomenon

Classification of Cyber-Aggression Cases Applying Machine Learning

The adoption of electronic social networks as an essential way of communication has become one of the most dangerous methods to hurt people’s feelings. The Internet and the proliferation of this kind of virtual community have caused severe negative consequences to the welfare of society, creating a social problem identified as cyber-aggression, or in some cases called cyber-bullying. This paper presents research to classify situations of cyber-aggression on social networks, specifically for Spanish-language users of Mexico. We applied Random Forest, Variable Importance Measures (VIMs), and OneR to support the classification of offensive comments in three particular cases of cyber-aggression: racism, violence based on sexual orientation, and violence against women. Experimental results with OneR improve the comment classification process of the three cyber-aggression cases, with more than 90% accuracy. The accurate classification of cyber-aggression comments can help to take measures to diminish this phenomenon

Prediction of Metabolic Syndrome in a Mexican Population Applying Machine Learning Algorithms

Metabolic syndrome is a health condition that increases the risk of heart diseases, diabetes, and stroke. The prognostic variables that identify this syndrome have already been defined by the World Health Organization (WHO), the National Cholesterol Education Program Third Adult Treatment Panel (ATP III) as well as by the International Diabetes Federation. According to these guides, there is some symmetry among anthropometric prognostic variables to classify abdominal obesity in people with metabolic syndrome. However, some appear to be more sensitive than others, nevertheless, these proposed definitions have failed to appropriately classify a specific population or ethnic group. In this work, we used the ATP III criteria as the framework with the purpose to rank the health parameters (clinical and anthropometric measurements, lifestyle data, and blood tests) from a data set of 2942 participants of Mexico City Tlalpan 2020 cohort, applying machine learning algorithms. We aimed to find the most appropriate prognostic variables to classify Mexicans with metabolic syndrome. The criteria of sensitivity, specificity, and balanced accuracy were used for validation. The ATP III using Waist-to-Height-Ratio (WHtR) as an anthropometric index for the diagnosis of abdominal obesity achieved better performance in classification than waist or body mass index. Further work is needed to assess its precision as a classification tool for Metabolic Syndrome in a Mexican population

A model based on the Naïve Bayes Classifier for teacher performance assessment

The evaluation of teacher performance is an important measurement process in Mexico's higher education institutions and around the world, because it allows feedback on the teacher’s performance to detect improvements in classes and propose strategies for the benefit of students' education. This paper describes the development and evaluation of a proposed computational model called SocialMining, which is based on the classifier algorithm Naïve Bayes to support the analysis of students' opinions from the process of teachers' performance evaluation, which is carried out through mobile devices. The mobile device revolutionizes processes in education; the proposal considers the use of this technology for the collection of data, taking advantage of processing capacity and acceptance by students in the process of education and learning. It also describes the development of a set of relevant affective terms of the teacher evaluation called corpus of subjectivity, which supports the Naïve Bayes algorithm to classify students' comments within the classes: positive, negative and neutral. To measure the comments classification performance of the SocialMining Computational Model, metrics such as the confusion matrix, precision, sensitivity, specificity and the ROC curve are used. Likewise, a study case is presented, which gathers new comments from students of the Polytechnic University of Aguascalientes (Mexico), in order to test the classification process performance of the proposed model. The results show that SocialMining Computational Model is feasible to be implemented in institutions to support Teacher Performance Assessment. Besides, our results show that Naïve Bayes can obtain a classification percentage very similar to those reported in recent works with related algorithms

Un modelo basado en el Clasificador Naïve Bayes para la evaluación del desempeño docente

Resumen basado en el de la publicaciónTítulo, resumen y palabras clave también en inglésMonográfico con el título: “La integración efectiva del dispositivo móvil en la educación y en el aprendizaje"Se describe el desarrollo y evaluación de un Modelo Computacional denominado SocialMining, basado en el algoritmo Naïve Bayes, para apoyar el análisis de las opiniones de los estudiantes en el proceso de la evaluación del desempeño docente, llevada a cabo mediante dispositivos móviles. Se utilizan éstos para la recopilación de datos, aprovechando su aceptación por parte de los estudiantes en el proceso de educación y aprendizaje. Asimismo, se describe el desarrollo de corpus de subjetividad, el cual consta de un conjunto de términos afectivos relevantes de la evaluación docente para apoyar al algoritmo Naïve Bayes en la clasificación de las opiniones de los estudiantes dentro de las clases: positivo, negativo y neutral. Para medir el desempeño del proceso de la clasificación del Modelo Computacional SocialMining, se utilizan métricas como la matriz de confusión, precisión y la curva de ROC (Receiver Operating Characteristic). Se presenta además un caso de estudio, en el cual se recolectan nuevas opiniones de estudiantes de la Universidad Politécnica de Aguascalientes (México) con el fin de probar el desempeño del modelo propuesto en la clasificación. Los resultados obtenidos consideran factible su implementación en instituciones de educación superior.ES

Características sociales, demográficas y de morbimortalidad de los casos atendidos por COVID-19 en el Instituto Nacional de Cardiología Ignacio Chávez. Un estudio transversal descriptivo

Introducción: La pandemia de enfermedad por coronavirus 2019 (COVID-19) trajo aparejadas una gran cantidad de consecuencias adversas para la salud pública con serias repercusiones socioeconómicas. En este estudio caracterizamos las condiciones sociales, demográficas y de morbimortalidad de los casos atendidos por COVID-19 en uno de los hospitales de referencia de coronavirus 2 del síndrome respiratorio agudo grave (SARS-CoV-2) en la Ciudad de México. Método: Se llevó a cabo un estudio transversal descriptivo en 259 pacientes egresados del Instituto Nacional de Cardiología Ignacio Chávez, entre el 11 de abril de 2020 y el 14 de marzo de 2021. Se utilizó un modelo de regresión logística multivariante para identificar la asociación entre variables sociodemográficas y clínicas. Se realizó una optimización mediante cálculos de máxima verosimilitud para elegir el mejor modelo compatible con los datos. El modelo de máxima verosimilitud fue evaluado mediante curvas ROC, estimadores de bondad de ajuste y análisis de multicolinealidad. Se infirieron patrones de comorbilidades estadísticamente significativos mediante la evaluación de una prueba hipergeométrica en las frecuencias de coocurrencia de pares de condiciones. Se implementó un análisis de redes para determinar los patrones de conectividad basado en la centralidad de grado, entre algunas comorbilidades y las variables de desenlace. Resultados: Las principales desventajas sociales de la población estudiada se relacionan con la falta de seguridad social (96.5%) y el rezago en las condiciones de vivienda (81%). Las variables asociadas a la probabilidad de sobrevivir fueron tener una menor edad (p < 0.0001), contar con más bienes materiales durables (p = 0.0034) y evitar: la neumonía (p = 0.0072), el choque séptico (p < 0.0001) y la insuficiencia respiratoria aguda (p < 0.0001); (AUROC: 91.5%). Las red de comorbilidades para los casos de supervivencia tienen un alto grado de conectividad entre padecimientos como las arritmias cardiacas e hipertensión arterial esencial (centralidad de grado: 90 y 78 respectivamente). Conclusiones: En vista de que entre los factores asociados a supervivencia existen variables clínicas, sociodemográficas y determinantes sociales de la salud, además de la edad, resulta imperativo considerar los diversos factores que puedan incidir o modificar el estado de salud de una población, sobre todo al abordar los fenómenos epidémicos emergentes como es el caso de la actual pandemia de COVID-19

Effects of social confinement during the first wave of COVID-19 in Mexico City

BackgroundThe COVID-19 pandemic led to global social confinement that had a significant impact on people's lives. This includes changes such as increased loneliness and isolation, changes in sleep patterns and social habits, increased substance use and domestic violence, and decreased physical activities. In some cases, it has increased mental health problems, such as anxiety, depression, and post-traumatic stress disorder.ObjectiveThe objective of this study is to analyze the living conditions that arose during social confinement in the first wave of COVID-19 within a group of volunteers in Mexico City.MethodsThis is a descriptive and cross-sectional analysis of the experiences of volunteers during social confinement from 20 March 2020 to 20 December 2020. The study analyzes the impact of confinement on family life, work, mental health, physical activity, social life, and domestic violence. A maximum likelihood generalized linear model is used to determine the association between domestic violence and demographic and health-related factors.ResultsThe findings indicate that social confinement had a significant impact on the participants, resulting in difficulties within families and vulnerable conditions for individuals. Gender and social level differences were observed in work and mental health. Physical activity and social life were also modified. We found that suffering from domestic violence was significantly associated with being unmarried (OR = 1.4454, p-value = 0.0479), lack of self-care in feeding habits (OR = 2.3159, p-value = 0.0084), and most notably, having suffered from a symptomatic COVID-19 infection (OR = 4.0099, p-value = 0.0009). Despite public policy to support vulnerable populations during confinement, only a small proportion of the studied population reported benefiting from it, suggesting areas for improvement in policy.ConclusionThe findings of this study suggest that social confinement during the COVID-19 pandemic had a significant impact on the living conditions of people in Mexico City. Modified circumstances on families and individuals, included increased domestic violence. The results can inform policy decisions to improve the living conditions of vulnerable populations during times of social confinement

Sleep Quality, Nutrient Intake, and Social Development Index Predict Metabolic Syndrome in the Tlalpan 2020 Cohort: A Machine Learning and Synthetic Data Study

This study investigated the relationship between Metabolic Syndrome (MetS), sleep disorders, the consumption of some nutrients, and social development factors, focusing on gender differences in an unbalanced dataset from a Mexico City cohort. We used data balancing techniques like SMOTE and ADASYN after employing machine learning models like random forest and RPART to predict MetS. Random forest excelled, achieving significant, balanced accuracy, indicating its robustness in predicting MetS and achieving a balanced accuracy of approximately 87%. Key predictors for men included body mass index and family history of gout, while waist circumference and glucose levels were most significant for women. In relation to diet, sleep quality, and social development, metabolic syndrome in men was associated with high lactose and carbohydrate intake, educational lag, living with a partner without marrying, and lack of durable goods, whereas in women, best predictors in these dimensions include protein, fructose, and cholesterol intake, copper metabolites, snoring, sobbing, drowsiness, sanitary adequacy, and anxiety. These findings underscore the need for personalized approaches in managing MetS and point to a promising direction for future research into the interplay between social factors, sleep disorders, and metabolic health, which mainly depend on nutrient consumption by region