Improving modular classification rule induction with G-Prism using dynamic rule term boundaries

Modular classification rule induction for predictive analytics is an alternative and expressive approach to rule induction as opposed to decision tree based classifiers. Prism classifiers achieve a similar classification accuracy compared with decision trees, but tend to overfit less, especially if there is noise in the data. This paper describes the development of a new member of the Prism family, the G-Prism classifier, which improves the classification performance of the classifier. G-Prism is different compared with the remaining members of the Prism family as it follows a different rule term induction strategy. G-Prism’s rule term induction strategy is based on Gauss Probability Density Distribution (GPDD) of target classes rather than simple binary splits (local discretisation). Two versions of G-Prism have been developed, one uses fixed boundaries to build rule terms from GPDD and the other uses dynamic rule term boundaries. Both versions have been compared empirically against Prism on 11 datasets using various evaluation metrics. The results show that in most cases both versions of G-Prism, especially G-Prism with dynamic boundaries, achieve a better classification performance compared with Prism

A rule-based classifier with accurate and fast rule term induction for continuous attributes

Rule-based classifiers are considered more expressive, human readable and less prone to over-fitting compared with decision trees, especially when there is noise in the data. Furthermore, rule-based classifiers do not suffer from the replicated subtree problem as classifiers induced by top down induction of decision trees (also known as `Divide and Conquer'). This research explores some recent developments of a family of rule-based classifiers, the Prism family and more particular G-Prism-FB and G-Prism-DB algorithms, in terms of local discretisation methods used to induce rule terms for continuous data. The paper then proposes a new algorithm of the Prism family based on a combination of Gauss Probability Density Distribution (GPDD), InterQuartile Range (IQR) and data transformation methods. This new rule-based algorithm, termed G-Rules-IQR, is evaluated empirically and outperforms other members of the Prism family in execution time, accuracy and tentative accuracy

Towards expressive modular rule induction for numerical attributes

The Prism family is an alternative set of predictive data mining algorithms to the more established decision tree data mining algorithms. Prism classifiers are more expressive and user friendly compared with decision trees and achieve a similar accuracy compared with that of decision trees and even outperform decision trees in some cases. This is especially the case where there is noise and clashes in the training data. However, Prism algorithms still tend to overfit on noisy data; this has led to the development of pruning methods which have allowed the Prism algorithms to generalise better over the dataset. The work presented in this paper aims to address the problem of overfitting at rule induction stage for numerical attributes by proposing a new numerical rule term structure based on the Gauss Probability Density Distribution. This new rule term structure is not only expected to lead to a more robust classifier, but also lowers the computational requirements as it needs to induce fewer rule terms

Review helpfulness prediction: Survey

Online reviews have become the major driving factor influencing purchasing behavior and patterns of social customers. However, it is difficult for customer to cover good reviews about any product or service according to massive amount of reviews latest years. Many previous researches provide innovative models about predicting review helpfulness in E-commerce websites. Some of these studies exploring the direct effect of review attributes on review helpfulness while others focused on reviewer’s attributes only. The main objective of this research is to review the most important attributes that have an affect on review helpfulness from many perspectives such as datasets, techniques, frameworks and evaluation methods of the experiments. The paper ends up with important findings about most attributes effect the review helpfulness such as Review Valenc

ReG-Rules: an explainable rule-based ensemble learner for classification

The learning of classification models to predict class labels of new and previously unseen data instances is one of the most essential tasks in data mining. A popular approach to classification is ensemble learning, where a combination of several diverse and independent classification models is used to predict class labels. Ensemble models are important as they tend to improve the average classification accuracy over any member of the ensemble. However, classification models are also often required to be explainable to reduce the risk of irreversible wrong classification. Explainability of classification models is needed in many critical applications such as stock market analysis, credit risk evaluation, intrusion detection, etc. Unfortunately, ensemble learning decreases the level of explainability of the classification, as the analyst would have to examine many decision models to gain insights about the causality of the prediction. The aim of the research presented in this paper is to create an ensemble method that is explainable in the sense that it presents the human analyst with a conditioned view of the most relevant model aspects involved in the prediction. To achieve this aim the authors developed a rule-based explainable ensemble classifier termed Ranked ensemble G-Rules (ReG-Rules) which gives the analyst an extract of the most relevant classification rules for each individual prediction. During the evaluation process ReG-Rules was evaluated in terms of its theoretical computational complexity, empirically on benchmark datasets and qualitatively with respect to the complexity and readability of the induced rule sets. The results show that ReG-Rules scales linearly, delivers a high accuracy and at the same time delivers a compact and manageable set of rules describing the predictions made

Radiographic assessment of endodontic mishaps in an undergraduate student clinic: a 2-year retrospective study

Objectives. The aim of this study was to compare the occurrence of instrumentation and obturation related endodontic procedural mishaps following the use of either, stainless steel hand or engine-driven rotary instrumentation techniques. Methods. From a computerized hospital database, a total of 730 dental patient records who had received endodontic treatment by undergraduate dental students between August 2018 to September 2020 were retrieved. The inclusion criteria were primary (non-surgical) endodontic treatment on permanent teeth with complete radiographic records. Following record screening, a final sample of n = 475 dental records were included. Radiographic records were evaluated for both instrumentation and obturation related mishaps. The data was analysed using multiple logistic regression analysis (α = 0.05). Results. Engine-driven rotary instrumentation resulted in a significant decrease in the overall occurrence of instrumentation related endodontic mishaps by 40% compared to hand instrumentation (Odds Ratio = 0.59 [0.36–0.97], p = 0.04). In particular, rotary instrumentation decreased ledge formation, perforation and obturation related mishaps, with minimal effect on the limitation of zipping. Conclusion. The use of rotary instrumentation techniques may reduce the incidence of instrumentation and obturation endodontic mishaps in the undergraduate dental clinic.Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R162), Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

Exome-wide association study to identify rare variants influencing COVID-19 outcomes : Results from the Host Genetics Initiative

Publisher Copyright: Copyright: © 2022 Butler-Laporte et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75–10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.Peer reviewe

The Saudi Critical Care Society practice guidelines on the management of COVID-19 in the ICU: Therapy section

BACKGROUND: The rapid increase in coronavirus disease 2019 (COVID-19) cases during the subsequent waves in Saudi Arabia and other countries prompted the Saudi Critical Care Society (SCCS) to put together a panel of experts to issue evidence-based recommendations for the management of COVID-19 in the intensive care unit (ICU). METHODS: The SCCS COVID-19 panel included 51 experts with expertise in critical care, respirology, infectious disease, epidemiology, emergency medicine, clinical pharmacy, nursing, respiratory therapy, methodology, and health policy. All members completed an electronic conflict of interest disclosure form. The panel addressed 9 questions that are related to the therapy of COVID-19 in the ICU. We identified relevant systematic reviews and clinical trials, then used the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach as well as the evidence-to-decision framework (EtD) to assess the quality of evidence and generate recommendations. RESULTS: The SCCS COVID-19 panel issued 12 recommendations on pharmacotherapeutic interventions (immunomodulators, antiviral agents, and anticoagulants) for severe and critical COVID-19, of which 3 were strong recommendations and 9 were weak recommendations. CONCLUSION: The SCCS COVID-19 panel used the GRADE approach to formulate recommendations on therapy for COVID-19 in the ICU. The EtD framework allows adaptation of these recommendations in different contexts. The SCCS guideline committee will update recommendations as new evidence becomes available

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types