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Comprehensible predictive modeling using regularized logistic regression and comorbidity based features
Gregor Štiglic, Petra Povalej Bržan, Nino Fijačko, Fei Wang, Alexandros Kalousis, Boris Delibašić, Zoran Obradović, 2015, original scientific article

Abstract: Different studies have demonstrated the importance of comorbidities to better understand the origin and evolution of medical complications. This study focuses on improvement of the predictive model interpretability based on simple logical features representing comorbidities. We use group lasso based feature interaction discovery followed by a post-processing step, where simple logic terms are added. In the final step, we reduce the feature set by applying lasso logistic regression to obtain a compact set of non-zero coefficients that represent a more comprehensible predictive model. The effectiveness of the proposed approach was demonstrated on a pediatric hospital discharge dataset that was used to build a readmission risk estimation model. The evaluation of the proposed method demonstrates a reduction of the initial set of features in a regression model by 72%, with a slight improvement in the Area Under the ROC Curve metric from 0.763 (95% CI: 0.755%0.771) to 0.769 (95% CI: 0.761%0.777). Additionally, our results show improvement in comprehensibility of the final predictive model using simple comorbidity based terms for logistic regression.
Keywords: predictive models, logistic regression, readmission classification, comorbidities
Published: 19.06.2017; Views: 628; Downloads: 278
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The impact of selected individual and external factors on the occurrence of severe injuries
Polona Tominc, Urban Šebjan, 2015, original scientific article

Abstract: The purpose of this paper is to contribute to the understanding of the importance of different determinants and their impact on the severity of injuries of individuals in road traffic accidents, so that measures that are supposed to prevent or reduce severe injury consequences can be developed. In this paper three research models have been built. Model I was built to study the impact of demographic factors (gender and age) on the individual’s likelihood to wear a seat-belt while Model II to study the impact of demographic factors (gender and age) and the impact of wearing a seat belt on the likelihood that fatal injuries of individuals in road traffic accidents occur. Model III was formed to study the impact of several environmental factors on the likelihood that the accident involves severe or fatal injuries of road traffic accident participants. Altogether our study revealed that middle-aged individuals (over 25 years and up to 65 years old) are less likely to wear a seatbelt and at the same time more likely to suffer fatal injuries in road traffic accidents. This is the result that implies that the targeted policy measures to the population between 25 and 65 years of age are needed to reduce the fatal injuries occurrence in Slovenia.
Keywords: road traffic accidents, seat belt, behavioural determinants, binomial logistic regression
Published: 06.06.2017; Views: 588; Downloads: 79
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Is trust in banks in Slovenia put to the test?
Sabina Taškar Beloglavec, Urban Šebjan, 2015, original scientific article

Abstract: The question of the banking system´s stability in connection to trust since the 2008 crisis has been the subject of many debates seeking to find permanent solutions to banking system problems, as the current situation affects bank customers´ behavior. This article examined trust in banks during the financial crisis and offers, via demographic variables, explanations as tow whether or not customers tend to withdraw their deposits during a crisis. The results contribute to banks´ decision-making regarding deposits management and understanding customers´ behavior, especially during a crisis. The results show a negative relationship between trust and deposit withdrawal intention, where gender and education level play an important role.
Keywords: trust, financial institution, bank, Slovenia, logistic regression
Published: 03.04.2017; Views: 572; Downloads: 255
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