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3 "Hossein Mahjub"
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Factors associated with mortality from tuberculosis in Iran: an application of a generalized estimating equation-based zero-inflated negative binomial model to national registry data
Fatemeh Sarvi, Abbas Moghimbeigi, Hossein Mahjub, Mahshid Nasehi, Mahmoud Khodadost
Epidemiol Health. 2019;41:e2019032.   Published online July 9, 2019
  • 9,154 View
  • 231 Download
AbstractAbstract PDF
Tuberculosis (TB) is a global public health problem that causes morbidity and mortality in millions of people per year. The purpose of this study was to examine the relationship of potential risk factors with TB mortality in Iran.
This cross-sectional study was performed on 9,151 patients with TB from March 2017 to March 2018 in Iran. Data were gathered from all 429 counties of Iran by the Ministry of Health and Medical Education and Statistical Center of Iran. In this study, a generalized estimating equation-based zero-inflated negative binomial model was used to determine the effect of related factors on TB mortality at the community level. For data analysis, R version 3.4.2 was used with the relevant packages.
The risk of mortality from TB was found to increase with the unemployment rate (βˆ=0.02), illiteracy (βˆ=0.04), household density per residential unit (βˆ=1.29), distance between the center of the county and the provincial capital (βˆ=0.03), and urbanization (βˆ=0.81). The following other risk factors for TB mortality were identified: diabetes (βˆ=0.02), human immunodeficiency virus infection (βˆ=0.04), infection with TB in the most recent 2 years (βˆ=0.07), injection drug use (βˆ=0.07), long-term corticosteroid use (βˆ=0.09), malignant diseases (βˆ=0.09), chronic kidney disease (βˆ=0.32), gastrectomy (βˆ=0.50), chronic malnutrition (βˆ=0.38), and a body mass index more than 10% under the ideal weight (βˆ=0.01). However, silicosis had no effect.
The results of this study provide useful information on risk factors for mortality from TB.
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Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross-sectional study
Maryam Kazemi, Abbas Moghimbeigi, Javad Kiani, Hossein Mahjub, Javad Faradmal
Epidemiol Health. 2016;38:e2016011.   Published online March 24, 2016
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  • 191 Download
  • 16 Citations
AbstractAbstract PDF
Diabetes is increasing in worldwide prevalence, toward epidemic levels. Diabetic neuropathy, one of the most common complications of diabetes mellitus, is a serious condition that can lead to amputation. This study used a multicategory support vector machine (MSVM) to predict diabetic peripheral neuropathy severity classified into four categories using patients’ demographic characteristics and clinical features.
In this study, the data were collected at the Diabetes Center of Hamadan in Iran. Patients were enrolled by the convenience sampling method. Six hundred patients were recruited. After obtaining informed consent, a questionnaire collecting general information and a neuropathy disability score (NDS) questionnaire were administered. The NDS was used to classify the severity of the disease. We used MSVM with both one-against-all and one-against-one methods and three kernel functions, radial basis function (RBF), linear, and polynomial, to predict the class of disease with an unbalanced dataset. The synthetic minority class oversampling technique algorithm was used to improve model performance. To compare the performance of the models, the mean of accuracy was used.
For predicting diabetic neuropathy, a classifier built from a balanced dataset and the RBF kernel function with a one-against-one strategy predicted the class to which a patient belonged with about 76% accuracy.
The results of this study indicate that, in terms of overall classification accuracy, the MSVM model based on a balanced dataset can be useful for predicting the severity of diabetic neuropathy, and it should be further investigated for the prediction of other diseases.
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Citations to this article as recorded by  
  • Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes
    Jingtong Huang, Andrea M. Yeung, David G. Armstrong, Ashley N. Battarbee, Jorge Cuadros, Juan C. Espinoza, Samantha Kleinberg, Nestoras Mathioudakis, Mark A. Swerdlow, David C. Klonoff
    Journal of Diabetes Science and Technology.2023; 17(1): 224.     CrossRef
  • A Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument
    Fahmida Haque, Mamun B. I. Reaz, Muhammad E. H. Chowdhury, Mohd Ibrahim bin Shapiai, Rayaz A. Malik, Mohammed Alhatou, Syoji Kobashi, Iffat Ara, Sawal H. M. Ali, Ahmad A. A. Bakar, Mohammad Arif Sobhan Bhuiyan
    Diagnostics.2023; 13(2): 264.     CrossRef
  • Prevalence and grade of diabetic peripheral neuropathy among known diabetic patients in rural Uganda
    Dalton Kambale Munyambalu, Idania Hildago, Yves Tibamwenda Bafwa, Charles Abonga Lagoro, Franck Katembo Sikakulya, Bienfait Mumbere Vahwere, Ephraim Dafiewhare, Lazaro Martinez, Fardous Abeya Charles
    Frontiers in Clinical Diabetes and Healthcare.2023;[Epub]     CrossRef
  • Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts
    Georgios Baskozos, Andreas C. Themistocleous, Harry L. Hebert, Mathilde M. V. Pascal, Jishi John, Brian C. Callaghan, Helen Laycock, Yelena Granovsky, Geert Crombez, David Yarnitsky, Andrew S. C. Rice, Blair H. Smith, David L. H. Bennett
    BMC Medical Informatics and Decision Making.2022;[Epub]     CrossRef
  • Predictors Associated with Type 2 Diabetes Mellitus Complications over Time: A Literature Review
    Marwa Elsaeed Elhefnawy, Siti Maisharah Sheikh Ghadzi, Sabariah Noor Harun
    Journal of Vascular Diseases.2022; 1(1): 13.     CrossRef
  • Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients
    Eiichiro Kanda, Atsushi Suzuki, Masaki Makino, Hiroo Tsubota, Satomi Kanemata, Koichi Shirakawa, Toshitaka Yajima
    Scientific Reports.2022;[Epub]     CrossRef
  • Prediction of Diabetic Neuropathy Using Machine Learning Techniques
    Jung Keun Hyun
    The Journal of Korean Diabetes.2022; 23(4): 238.     CrossRef
  • Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data
    Mathieu Ravaut, Hamed Sadeghi, Kin Kwan Leung, Maksims Volkovs, Kathy Kornas, Vinyas Harish, Tristan Watson, Gary F. Lewis, Alanna Weisman, Tomi Poutanen, Laura Rosella
    npj Digital Medicine.2021;[Epub]     CrossRef
  • Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification
    Fahmida Haque, Mamun Bin Ibne Reaz, Muhammad Enamul Hoque Chowdhury, Geetika Srivastava, Sawal Hamid Md Ali, Ahmad Ashrif A. Bakar, Mohammad Arif Sobhan Bhuiyan
    Diagnostics.2021; 11(5): 801.     CrossRef
  • Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary data
    Md. Maniruzzaman, Md. Merajul Islam, Md. Jahanur Rahman, Md. Al Mehedi Hasan, Jungpil Shin
    Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2021; 15(5): 102263.     CrossRef
  • Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques
    Dae Youp Shin, Bora Lee, Won Sang Yoo, Joo Won Park, Jung Keun Hyun
    Journal of Clinical Medicine.2021; 10(19): 4576.     CrossRef
  • T1DMicro: A Clinical Risk Calculator for Type 1 Diabetes Related Microvascular Complications
    Paul Minh Huy Tran, Eileen Kim, Lynn Kim Hoang Tran, Bin Satter Khaled, Diane Hopkins, Melissa Gardiner, Jennifer Bryant, Risa Bernard, John Morgan, Bruce Bode, John Chip Reed, Jin-Xiong She, Sharad Purohit
    International Journal of Environmental Research and Public Health.2021; 18(21): 11094.     CrossRef
  • Diagnosing thyroid disorders: Comparison of logistic regression and neural network models
    Shiva Borzouei, Hossein Mahjub, NegarAsaad Sajadi, Maryam Farhadian
    Journal of Family Medicine and Primary Care.2020; 9(3): 1470.     CrossRef
  • Thyroid disorder diagnosis based on Mamdani fuzzy inference system classifier
    Negar Asaad Sajadi, Hossein Mahjub, Shiva Borzouei, Maryam Farhadian
    Koomesh Journal.2020; 22(1): 107.     CrossRef
  • Identification of risk factors for patients with diabetes: diabetic polyneuropathy case study
    Oleg Metsker, Kirill Magoev, Alexey Yakovlev, Stanislav Yanishevskiy, Georgy Kopanitsa, Sergey Kovalchuk, Valeria V. Krzhizhanovskaya
    BMC Medical Informatics and Decision Making.2020;[Epub]     CrossRef
  • Diagnosis of hypothyroidism using a fuzzy rule-based expert system
    Negar Asaad Sajadi, Shiva Borzouei, Hossein Mahjub, Maryam Farhadian
    Clinical Epidemiology and Global Health.2019; 7(4): 519.     CrossRef
Estimation of the Frequency of Intravenous Drug Users in Hamadan City, Iran, Using the Capture-recapture Method
Salman Khazaei, Jalal Poorolajal, Hossein Mahjub, Nader Esmailnasab, Mohammad Mirzaei
Epidemiol Health. 2012;34:e2012006.   Published online October 31, 2012
  • 12,834 View
  • 100 Download
  • 6 Citations
AbstractAbstract PDF

The number of illicit drug users is prone to underestimation. This study aimed to use the capture-recapture method as a statistical procedure for measuring the prevalence of intravenous drug users (IDUs) by estimating the number of unknown IDUs not registered by any of the registry centers.


This study was conducted in Hamadan City, the west of Iran, in 2012. Three incomplete data sources of IDUs, with partial overlapping data, were assessed including: (a) Volunteer Counseling and Testing Centers (VCTCs); (b) Drop in Centers (DICs); and (c) Outreach Teams (ORTs). A log-linear model was applied for the analysis of three-sample capture-recapture results. Two information criteria were used for model selection including Akaike's Information Criterion and the Bayesian Information Criterion.


Out of 1,478 IDUs registered by three centers, 48% were identified by VCTCs, 32% by DICs, and 20% by ORTs. After exclusion of duplicates, 1,369 IDUs remained. According to our findings, there were 9,964 (95% CI, 6,088 to 17,636) IDUs not identified by any of the centers. Hence, the real number of IDUs is expected to be 11,333. Based on these findings, the overall completeness of the three data sources was around 12% (95% CI, 7% to 18%).


There was a considerable number of IDUs not identified by any of the centers. Although the capture-recapture method is a useful and practical approach for estimating unknown populations, due to the assumptions and limitations of the method, the results must be interpreted with caution.

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Citations to this article as recorded by  
  • The prevalence of opioid use disorder in Kentucky’s counties: A two-year multi-sample capture-recapture analysis
    Katherine Thompson, Joshua A. Barocas, Chris Delcher, Jungjun Bae, Lindsey Hammerslag, Jianing Wang, Redonna Chandler, Jennifer Villani, Sharon Walsh, Jeffery Talbert
    Drug and Alcohol Dependence.2023; 242: 109710.     CrossRef
  • Estimating the Population Size of Female Sex Worker Population in Tehran, Iran: Application of Direct Capture–Recapture Method
    Manoochehr Karami, Salman Khazaei, Jalal Poorolajal, Alireza Soltanian, Mansour Sajadipoor
    AIDS and Behavior.2017; 21(8): 2394.     CrossRef
  • Using the capture-recapture method to estimate the human immunodeficiency virus-positive population
    Jalal Poorolajal, Younes Mohammadi, Farzad Farzinara
    Epidemiology and Health.2017; 39: e2017042.     CrossRef
  • Estimation of perinatal mortality rate for institutional births in Rajasthan state, India, using capture-recapture technique
    P. K. Mony, B. Varghese, T. Thomas
    BMJ Open.2015; 5(3): e005966.     CrossRef
  • Estimating the size of the population of persons who inject drugs in the island of Montréal, Canada, using a six-source capture–recapture model
    Pascale Leclerc, Alain C. Vandal, Aïssatou Fall, Julie Bruneau, Élise Roy, Suzanne Brissette, Chris Archibald, Nelson Arruda, Carole Morissette
    Drug and Alcohol Dependence.2014; 142: 174.     CrossRef
  • Heterogeneity and behavioral response in continuous time capture–recapture, with application to street cannabis use in Italy
    Alessio Farcomeni, Daria Scacciatelli
    The Annals of Applied Statistics.2013;[Epub]     CrossRef

Epidemiol Health : Epidemiology and Health