COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule Mining

Association rule mining plays a crucial role in the medical area in discovering interesting relationships among the attributes of a data set. Traditional association rule mining algorithms such as Apriori, FP growth, or Eclat require considerable computational resources and generate large volumes of rules. Moreover, these techniques depend on user-defined thresholds which can inadvertently cause the algorithm to omit some interesting rules. In order to solve such challenges, we propose an evolutionary multi-objective algorithm based on NSGA-II to guide the mining process in a data set composed of 15.5 million records with official data describing the COVID-19 pandemic in Mexico. We tested different scenarios optimizing classical and causal estimation measures in four waves, defined as the periods of time where the number of people with COVID-19 increased. The proposed contributions generate, recombine, and evaluate patterns, focusing on recovering promising high-quality rules with actionable cause–effect relationships among the attributes to identify which groups are more susceptible to disease or what combinations of conditions are necessary to receive certain types of medical care

Studying the Effect of Robustness Measures in Offline Parameter Tuning for Estimating the Performance of MOEA/D

International audienceOffline parameter tuning (OPT) of multi-objective evolutionary algorithms (MOEAs) has the goal of finding an appropriate set of parameters for solving a large number of problems. According to the no free lunch theorem (NFL), no algorithm can have the best performance in all classes of optimization problems. However, it is possible to find an appropriate set of parameters of an algorithm for solving a particular class of problems. For that sake, we need to study how to estimate the aggregation quality function for an algorithmic configuration assessed on a set of optimization problems. In this paper, we study robustness measures for dealing with the parameter settings of stochastic algorithms. We focus on decomposition-based MOEAs and we propose to tune scalarizing functions for solving some classes of problems based on the Pareto front shapes using up to 7 objective functions. Based on our experimental results, we were able to derive interesting guidelines to evaluate the quality of algorithmic configurations using a combination of descriptive statistics

QSTR Modeling to Find Relevant DFT Descriptors Related to the Toxicity of Carbamates

Compounds containing carbamate moieties and their derivatives can generate serious public health threats and environmental problems due their high potential toxicity. In this study, a quantitative structure–toxicity relationship (QSTR) model has been developed by using one hundred seventy-eight carbamate derivatives whose toxicities in rats (oral administration) have been evaluated. The QSRT model was rigorously validated by using either tested or untested compounds falling within the applicability domain of the model. A structure-based evaluation by docking from a series of carbamates with acetylcholinesterase (AChE) was carried out. The toxicity of carbamates was predicted using physicochemical, structural, and quantum molecular descriptors employing a DFT approach. A statistical treatment was developed; the QSRT model showed a determination coefficient (R2) and a leave-one-out coefficient (Q2LOO) of 0.6584 and 0.6289, respectively