Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists

The objective of this study is to analysis of accident of motorcyclists on Bogotá roads in Colombia. For detection of conditions related to crashes and their severity, the proposed model develops the strategies to enhance road safety. In this context, data mining and machine learning techniques are used to investigate 34,232 accidents by motorcyclists during January 2013 to February 2018. Both the Genetic algorithm and simulated annealing are applied in conjunction with mining rules (support, confidence, lift, and comprehensibility) as per objectives of the problem. The application of a hybrid algorithm allows for the creation and definition of optimal hierarchical decision rules for the prediction of the severity of motorcycle traffic accidents. The proposed method yields good results in the metrics of recall (90.07%), precision (89.87%), and accuracy (90.06%) on the data set. The results increase the prediction by 20–21% in comparisons with the following methods: Decision Trees (CART, ID3, and C4.5), Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), Naive Bayes, Neural Networks, Random Forest, and Random Tree. The proposed method defines 11 rules for the prediction of accidents with material damage, 24 rules with injuries, and 12 rules with fatalities. The variables with the most recurrence in the definition of rules are time, weather and road conditions, and the number of victims involved in the accidents. Finally, the interactions of the conditions and characteristics presented in motorcycle accidents are analyzed which contribute to the definition of countermeasures for road safety. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature

A method to rationalize the product portfolio in retail stores

Retail store operations face a variety of challenges and complexities. Determining the best assortment is the main problem in the retail store. This re-search presents a practical methodology for the analysis of products in the assortment with the goal of reducing the excess items and improve sales and profit margin of a retail store without affecting customer satisfaction. The methodology integrates 6 steps that allow to optimize products of a portfolio in categories, sub categories and segments, through Pareto analysis and clustering analysis using the BCG matrix. The methodology was applied in an independent supermarket. The results in the case of the application for non-perishable products, allowed to identify a set of different products (n = 152), of which they were prioritized in a subcategory (oils) in which 90 products were prioritized. In the example, it shows how 21 products have significant results in the variety of products. The combination of the global and local category of the product, the net profit, the inventory rotation and the participation of the growth provides a multifactorial analysis in the decision-making to supply with products a retail store seeking to increase the level of service and maximizing profits

Using Data-mining Techniques for the prediction of the severity of road crashes in Cartagena, Colombia

Objective: Analyze the road crashes in Cartagena (Colombia) and the factors associated with the collision and severity. The aim is to establish a set of rules for defining countermeasures to improve road safety. Methods: Data mining and machine learning techniques were used in 7894 traffic accidents from 2016 to 2017. The severity was determined between low (84%) and high (16%). Five classification algorithms to predict the accident severity were applied with WEKA Software (Waikato Environment for Knowledge Analysis). Including Decision Tree (DT-J48), Rule Induction (PART), Support Vector Machines (SVMs), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The effectiveness of each algorithm was implemented using cross-validation with 10-fold. Decision rules were defined from the results of the different methods. Results: The methods applied are consistent and similar in the overall results of precision, accuracy, recall, and area under the ROC curve. Conclusions: 12 decision rules were defined based on the methods applied. The rules defined show motorcyclists, cyclists, including pedestrians, as the most vulnerable road users. Men and women motorcyclists between 20–39 years are prone in accidents with high severity. When a motorcycle or cyclist is not involved in the accident, the probable severity is low

Dataset of road crashes in motorcyclists in Bogotá

The dataset was consolidated with 175,245 road accidents and the report of 337,828 road actors involved in collisions, crashes, and fatalities between January 2013 and February 2018 in Bogotá. The data was compiled, processed, and enriched with additional information about infrastructure and weather conditions. The data consists of 35,693 motorcyclist accident records.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Dataset of road crashes in motorcyclists in Bogotá (2013-2018)

The dataset was consolidated with 175,245 road accidents and the report of 337,828 road actors involved in collisions, crashes, and fatalities between January 2013 and February 2018 in Bogotá. The data was compiled, processed, and enriched with additional information about infrastructure and weather conditions. The data consists of 35,693 motorcyclist accident records.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Ezetimibe added to statin therapy after acute coronary syndromes

BACKGROUND: Statin therapy reduces low-density lipoprotein (LDL) cholesterol levels and the risk of cardiovascular events, but whether the addition of ezetimibe, a nonstatin drug that reduces intestinal cholesterol absorption, can reduce the rate of cardiovascular events further is not known. METHODS: We conducted a double-blind, randomized trial involving 18,144 patients who had been hospitalized for an acute coronary syndrome within the preceding 10 days and had LDL cholesterol levels of 50 to 100 mg per deciliter (1.3 to 2.6 mmol per liter) if they were receiving lipid-lowering therapy or 50 to 125 mg per deciliter (1.3 to 3.2 mmol per liter) if they were not receiving lipid-lowering therapy. The combination of simvastatin (40 mg) and ezetimibe (10 mg) (simvastatin-ezetimibe) was compared with simvastatin (40 mg) and placebo (simvastatin monotherapy). The primary end point was a composite of cardiovascular death, nonfatal myocardial infarction, unstable angina requiring rehospitalization, coronary revascularization ( 6530 days after randomization), or nonfatal stroke. The median follow-up was 6 years. RESULTS: The median time-weighted average LDL cholesterol level during the study was 53.7 mg per deciliter (1.4 mmol per liter) in the simvastatin-ezetimibe group, as compared with 69.5 mg per deciliter (1.8 mmol per liter) in the simvastatin-monotherapy group (P<0.001). The Kaplan-Meier event rate for the primary end point at 7 years was 32.7% in the simvastatin-ezetimibe group, as compared with 34.7% in the simvastatin-monotherapy group (absolute risk difference, 2.0 percentage points; hazard ratio, 0.936; 95% confidence interval, 0.89 to 0.99; P = 0.016). Rates of pre-specified muscle, gallbladder, and hepatic adverse effects and cancer were similar in the two groups. CONCLUSIONS: When added to statin therapy, ezetimibe resulted in incremental lowering of LDL cholesterol levels and improved cardiovascular outcomes. Moreover, lowering LDL cholesterol to levels below previous targets provided additional benefit