Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

An Evaluation Of Authorship Attribution Using Random Forests

Electronic text (e-text) stylometry aims at identifying the writing style of authors of electronic texts, such as electronic documents, blog posts, tweets, etc. Identifying such styles is quite attractive for identifying authors of disputed e-text, identifying their profile attributes (e.g. gender, age group, etc), or even enhancing services such as search engines and recommender systems. Despite the success of Random Forests, its performance has not been evaluated on Author Attribtion problems. In this paper, we present an evaluation of Random Forests in the problem domain of Authorship Attribution. Additionally, we have taken advantage of Random Forests\u27 robustness against noisy features by extracting a diverse set of features from evaluated e-texts. Interestingly, the resultant model achieved the highest classification accuracy in all problems, except one where it misclassified only a single instance

An empirical evaluation for feature selection methods in phishing email classification

Phishing email detection is highly dependent on the accuracy of anti-phishing classifiers. Classifiers that use Machine-Learning techniques achieve highest phishing email classification accuracy results according to the literature. Using effective features in Machine-Learning is a critical step in raising classifiers detection accuracy. This study aims at evaluating a number of feature subset selection methods as they relate to the phishing email classification domain. In order to perform this study, a total of 47 classification features were constructed as previously proposed in the literature. The primary outcome of this study is that the Wrapper evaluator and the Best-First: Forward searching method resulted in finding the most effective features subset among all other evaluated methods. This study addresses the gap that exists between fragmented literature items by evaluating them together following common evaluation metrics. Using the best performing feature selection method, an effective features subset was found among the 47 previously proposed features, which resulted in a highly accurate anti-phishing email classifier with an f1 score of 99.396%. This also shows that a highly competitive anti-phishing email classifier can still be constructed by only using existing Machine-Learning techniques and previously proposed features if an effective features subset is found

Enhancing Phishing E-Mail Classifiers: A Lexical URL Analysis Approach

This is a study that focuses on enhancing the mitigation of bulk phishing email messsages (i.e. email messages with generic socially engineered content that target a broad range of recipients). This study is based on a phishing website detection technique that we have proposed previously. The previously proposed technique was able to achieve 97% of classification accuracy of anti-phishing email filters enhance when they incorporate the proposed lexical URL analysis technique. To evaluate the claims, a highly accurate anti-phishing email classifier is constructed and tested against publicly available phishing and legitimate email data sets