Online) An Open Access

ABSTRACT Improvement in the application and data collection, using computer and also public uses of web and internet as world informing system, introduce data mining as a useful instrument to help solving complicated problems. In recent years, validation is changed to an important issue for financial institutes such as banks and insurance companies. The validation issue poses as a classification problem and is aimed at extraction suitable and efficient model which categorize customers according to their characteristics into two categories: good and bad. In the past in order to create validation issues, classical methods of data mining like linear regression and the analysis of linear point was used. But recently instruments of intelligence emotion and machine learning are noticed by researchers of validation issues. Different models, abstract and hybrid, is posed for this problem in order to create a high performance model. In this regards efforts are going on. The purpose of this thesis is identifying the factors (variables) influencing the behavior of costumers applying for post banks credit facility and provide a model for customers , validation, using neural network and particle swarm algorithm. Datasets used by post bank of Tehran, for each of these methods measure the error rate and the execution time will be calculated and compared. The results indicate the superiority of the combined neural network model and Particle Swarm Algorithm and Matlab software is used for simulation. Finally, conclusions and suggestions for future research in this thesis have ended. Keywords: Data Mining, Validation, Neural Network, Genetic Algorithm, Particle Swarm Algorithm INTRODUCTION The advent of information and communication technology, and overflow of information about customers on one hand, daily development of credit facilities market, and also increased rate of delayed or irreversible demands, have led the credit institutions to exploit some new models in order to evaluate credit of their customers. In recent years, the field of data mining has grabbed the attention of scientific and industrial communities of validation. In fact, since methods of information gathering and saving them in today's world is simple and cheap, it has led various businesses to face a large volume of data and seek some methods and techniques, so that they would be able to use these collected informatio

Machine Learning Methods for Structural Brain MRIs: Applications for Alzheimer’s Disease and Autism Spectrum Disorder

This thesis deals with the development of novel machine learning applications to automatically detect brain disorders based on magnetic resonance imaging (MRI) data, with a particular focus on Alzheimer’s disease and the autism spectrum disorder. Machine learning approaches are used extensively in neuroimaging studies of brain disorders to investigate abnormalities in various brain regions. However, there are many technical challenges in the analysis of neuroimaging data, for example, high dimensionality, the limited amount of data, and high variance in that data due to many confounding factors. These limitations make the development of appropriate computational approaches more challenging. To deal with these existing challenges, we target multiple machine learning approaches, including supervised and semi-supervised learning, domain adaptation, and dimensionality reduction methods.In the current study, we aim to construct effective biomarkers with sufficient sensitivity and specificity that can help physicians better understand the diseases and make improved diagnoses or treatment choices. The main contributions are 1) development of a novel biomarker for predicting Alzheimer’s disease in mild cognitive impairment patients by integrating structural MRI data and neuropsychological test results and 2) the development of a new computational approach for predicting disease severity in autistic patients in agglomerative data by automatically combining structural information obtained from different brain regions.In addition, we investigate various data-driven feature selection and classification methods for whole brain, voxel-based classification analysis of structural MRI and the use of semi-supervised learning approaches to predict Alzheimer’s disease. We also analyze the relationship between disease-related structural changes and cognitive states of patients with Alzheimer’s disease.The positive results of this effort provide insights into how to construct better biomarkers based on multisource data analysis of patient and healthy cohorts that may enable early diagnosis of brain disorders, detection of brain abnormalities and understanding effective processing in patient and healthy groups. Further, the methodologies and basic principles presented in this thesis are not only suited to the studied cases, but also are applicable to other similar problems

Measuring Iran’s success in achieving Millennium Development Goal 4: a systematic analysis of under-5 mortality at national and subnational levels from 1990 to 2015

Background Child mortality as one of the key Millennium Development Goals (MDG 4—to reduce child mortality by two-thirds from 1990 to 2015), is included in the Sustainable Development Goals (SDG 3, target 2—to reduce child mortality to fewer than 25 deaths per 1000 livebirths for all countries by 2030), and is a key indicator of the health system in every country. In this study, we aimed to estimate the level and trend of child mortality from 1990 to 2015 in Iran, to assess the progress of the country and its provinces toward these goals. Methods We used three different data sources: three censuses, a Demographic and Health Survey (DHS), and 5-year data from the death registration system. We used the summary birth history data from four data sources (the three censuses and DHS) and used maternal age cohort and maternal age period methods to estimate the trends in child mortality rates, combining the estimates of these two indirect methods using Loess regression. We also used the complete birth history method to estimate child mortality rate directly from DHS data. Finally, to synthesise different trends into a single trend and calculate uncertainty intervals (UI), we used Gaussian process regression. Findings Under-5 mortality rates (deaths per 1000 livebirths) at the national level in Iran in 1990, 2000, 2010, and 2015 were 63·6 (95% UI 63·1–64·0), 38·8 (38·5–39·2), 24·9 (24·3–25·4), and 19·4 (18·6–20·2), respectively. Between 1990 and 2015, the median annual reduction and total overall reduction in these rates were 4·9% and 70%, respectively. At the provincial level, the difference between the highest and lowest child mortality rates in 1990, 2000, and 2015 were 65·6, 40·4, and 38·1 per 1000 livebirths, respectively. Based on the MDG 4 goal, five provinces had not decreased child mortality by two-thirds by 2015. Furthermore, six provinces had not reached SDG 3 (target 2). Interpretation Iran and most of its provinces achieved MDG 4 and SDG 3 (target 2) goals by 2015. However, at the subnational level in some provinces, there is substantial inequity. Local policy makers should use effective strategies to accelerate the reduction of child mortality for these provinces by 2030. Possible recommendations for such strategies include enhancing the level of education and health literacy among women, tackling sex discrimination, and improving incomes for families

Effect of topical honey application along with intralesional injection of glucantime in the treatment of cutaneous leishmaniasis

<p>Abstract</p> <p>Background</p> <p>Leishmaniasis is an endemic disease in Iran. Although many treatments have been suggested for this disease, there hasn't been an effective and safe treatment yet. Regarding the healing effect of honey in the chronic ulcers and its reported therapeutic effect in cutaneous leishmaniasis, we performed a study to better evaluate the efficacy of honey in cutaneous leishmaniasis and its final scar.</p> <p>Methods</p> <p>In a prospective clinical trial, 100 patients with confirmed cutaneous leishmaniasis were selected and randomized into 2 groups. Group A were treated with topical honey twice daily along with intralesional injection of glucantime once weekly until complete healing of the ulcer or for maximum of 6 weeks. Group B were treated with intralesional injection of glucantime alone until complete healing of the ulcer or for a maximum of 6 weeks, too. The patients were followed for 4 months. The collected data were analyzed statistically using statistical tests including Chi-square, Mann Whitney and Kaplan – Mayer tests.</p> <p>Results</p> <p>In this study, 45 patients that had cutaneous leishmaniasis were treated with intralesional glucantime alone and 45 patients were treated with topical honey and glucantime . Ten patients left out the study. In the glucantime alone treated group, 32 patients (71.1%) had complete cure whereas in the group treated with both glucantime & topical honey, 23 patients (51.1%) achieved complete cure. This difference was significant statistically (p = 0.04).</p> <p>Conclusion</p> <p>Further studies to better clarify the efficacy of honey in cutaneous leishmaniasis is needed. We suggest that in another study, the efficacy of honey with standardized level of antibacterial activity is evaluated against cutaneous leishmaniasis.</p

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org

The unfinished agenda of communicable diseases among children and adolescents before the COVID-19 pandemic, 1990-2019: a systematic analysis of the Global Burden of Disease Study 2019

BACKGROUND: Communicable disease control has long been a focus of global health policy. There have been substantial reductions in the burden and mortality of communicable diseases among children younger than 5 years, but we know less about this burden in older children and adolescents, and it is unclear whether current programmes and policies remain aligned with targets for intervention. This knowledge is especially important for policy and programmes in the context of the COVID-19 pandemic. We aimed to use the Global Burden of Disease (GBD) Study 2019 to systematically characterise the burden of communicable diseases across childhood and adolescence. METHODS: In this systematic analysis of the GBD study from 1990 to 2019, all communicable diseases and their manifestations as modelled within GBD 2019 were included, categorised as 16 subgroups of common diseases or presentations. Data were reported for absolute count, prevalence, and incidence across measures of cause-specific mortality (deaths and years of life lost), disability (years lived with disability [YLDs]), and disease burden (disability-adjusted life-years [DALYs]) for children and adolescents aged 0-24 years. Data were reported across the Socio-demographic Index (SDI) and across time (1990-2019), and for 204 countries and territories. For HIV, we reported the mortality-to-incidence ratio (MIR) as a measure of health system performance. FINDINGS: In 2019, there were 3·0 million deaths and 30·0 million years of healthy life lost to disability (as measured by YLDs), corresponding to 288·4 million DALYs from communicable diseases among children and adolescents globally (57·3% of total communicable disease burden across all ages). Over time, there has been a shift in communicable disease burden from young children to older children and adolescents (largely driven by the considerable reductions in children younger than 5 years and slower progress elsewhere), although children younger than 5 years still accounted for most of the communicable disease burden in 2019. Disease burden and mortality were predominantly in low-SDI settings, with high and high-middle SDI settings also having an appreciable burden of communicable disease morbidity (4·0 million YLDs in 2019 alone). Three cause groups (enteric infections, lower-respiratory-tract infections, and malaria) accounted for 59·8% of the global communicable disease burden in children and adolescents, with tuberculosis and HIV both emerging as important causes during adolescence. HIV was the only cause for which disease burden increased over time, particularly in children and adolescents older than 5 years, and especially in females. Excess MIRs for HIV were observed for males aged 15-19 years in low-SDI settings. INTERPRETATION: Our analysis supports continued policy focus on enteric infections and lower-respiratory-tract infections, with orientation to children younger than 5 years in settings of low socioeconomic development. However, efforts should also be targeted to other conditions, particularly HIV, given its increased burden in older children and adolescents. Older children and adolescents also experience a large burden of communicable disease, further highlighting the need for efforts to extend beyond the first 5 years of life. Our analysis also identified substantial morbidity caused by communicable diseases affecting child and adolescent health across the world. FUNDING: The Australian National Health and Medical Research Council Centre for Research Excellence for Driving Investment in Global Adolescent Health and the Bill & Melinda Gates Foundation

Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin

Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019

Background: Updated data on chronic respiratory diseases (CRDs) are vital in their prevention, control, and treatment in the path to achieving the third UN Sustainable Development Goals (SDGs), a one-third reduction in premature mortality from non-communicable diseases by 2030. We provided global, regional, and national estimates of the burden of CRDs and their attributable risks from 1990 to 2019. Methods: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we estimated mortality, years lived with disability, years of life lost, disability-adjusted life years (DALYs), prevalence, and incidence of CRDs, i.e. chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease and pulmonary sarcoidosis, and other CRDs, from 1990 to 2019 by sex, age, region, and Socio-demographic Index (SDI) in 204 countries and territories. Deaths and DALYs from CRDs attributable to each risk factor were estimated according to relative risks, risk exposure, and the theoretical minimum risk exposure level input. Findings: In 2019, CRDs were the third leading cause of death responsible for 4.0 million deaths (95% uncertainty interval 3.6–4.3) with a prevalence of 454.6 million cases (417.4–499.1) globally. While the total deaths and prevalence of CRDs have increased by 28.5% and 39.8%, the age-standardised rates have dropped by 41.7% and 16.9% from 1990 to 2019, respectively. COPD, with 212.3 million (200.4–225.1) prevalent cases, was the primary cause of deaths from CRDs, accounting for 3.3 million (2.9–3.6) deaths. With 262.4 million (224.1–309.5) prevalent cases, asthma had the highest prevalence among CRDs. The age-standardised rates of all burden measures of COPD, asthma, and pneumoconiosis have reduced globally from 1990 to 2019. Nevertheless, the age-standardised rates of incidence and prevalence of interstitial lung disease and pulmonary sarcoidosis have increased throughout this period. Low- and low-middle SDI countries had the highest age-standardised death and DALYs rates while the high SDI quintile had the highest prevalence rate of CRDs. The highest deaths and DALYs from CRDs were attributed to smoking globally, followed by air pollution and occupational risks. Non-optimal temperature and high body-mass index were additional risk factors for COPD and asthma, respectively. Interpretation: Albeit the age-standardised prevalence, death, and DALYs rates of CRDs have decreased, they still cause a substantial burden and deaths worldwide. The high death and DALYs rates in low and low-middle SDI countries highlights the urgent need for improved preventive, diagnostic, and therapeutic measures. Global strategies for tobacco control, enhancing air quality, reducing occupational hazards, and fostering clean cooking fuels are crucial steps in reducing the burden of CRDs, especially in low- and lower-middle income countries

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe