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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 17750

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Special Issue Editors

Dr. Hassan Doosti

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Leading Guest Editor

Department of Mathematics and Statistics, Macquarie University, Sydney 2109, Australia
Interests: statistics in medicine
Special Issues, Collections and Topics in MDPI journals

Dr. Hashem Salarzadeh Jenatabadi

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Assistant Guest Editor

Department of Science and Technology Studies, Faculty of Science, University of Malaya
Interests: Statistical modeling; Structural equation modeling; Statistics in Public Health

Dr. Kobra Etminani

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Assistant Guest Editor

Center for Applied Intelligent System Research (CAISR), School of Information Technology, Halmstad University, Halmstad, Sweden
Interests: machine learning; artificial intelligence; data mining; healthcare informatics
Special Issues, Collections and Topics in MDPI journals

Dr. Nino Kordzakhia

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Assistant Guest Editor

Macquarie University, Department of Mathematics and Statistics, Australia
Interests: Statistical Methodology; Statistial Modelling in Health, Finance and Environmetrics

Special Issue Information

Dear Colleagues,

Nonparametric statistics and machine learning are two popular research fields which investigate the relationship between response variables and a set of risk factors in Medical Studies. Recently, both of these approaches have seen a significant development. The main target for this issue is to highlight new developments in these two areas mainly focusing on applications in health research.

Dr. Hassan Doosti
Leading Guest Editor
Dr. Nino Kordzakhia
Dr. Kobra Etminani
Dr. Hashem Salarzadeh Jenatabadi
Assistant Guest Editors

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Keywords

Nonparametric Regression Function Estimation
Machine learning
Artificial intelligence
data mining

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Published Papers (5 papers)

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Research

10 pages, 942 KiB

Open AccessArticle

Introducing Copula as a Novel Statistical Method in Psychological Analysis

by Elham Dehghani, Somayeh Hadad Ranjbar, Moharram Atashafrooz, Hossein Negarestani, Amir Mosavi and Levente Kovacs

Int. J. Environ. Res. Public Health 2021, 18(15), 7972; https://doi.org/10.3390/ijerph18157972 - 28 Jul 2021

Cited by 7 | Viewed by 2319

Abstract

During the past decades, the relationship between various psychological parameters had been studied in detail. However, the dependency structure of correlated parameters was rarely investigated. Knowing the dependence structure helps in finding the probability matrix of the interaction between the parameters. In this research, a novel approach was introduced in psychological analysis using copula functions. For this purpose, the self-esteem and anxiety of 141 university students in Iran were extracted using the Coopersmith Self-esteem Inventory and the Zang Anxiety Scale. Then the dependence structure of self-esteem and anxiety were established using copula functions. The Frank copula achieved the best fit for the joint variables of self-esteem and anxiety. Finally, the probability matrix of different classes of anxiety, taking into account self-esteem classes, was extracted. The results indicated that poor self-esteem leads to severe or very severe anxiety, with more than 98% probability, while strong self-esteem may lead to normal and mild anxiety, with about 80% probability. It can be concluded that the method was promising, and that copula functions can open a window to the dependence structure analysis of psychological parameters. Full article

(This article belongs to the Special Issue Nonparametric Statistics and Machine Learning with Applications in Health Studies)

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12 pages, 487 KiB

Open AccessArticle

Pre-Emptive and Non-Pre-Emptive Goal Programming Problems for Optimal Menu Planning in Diet Management of Indian Diabetes Mellitus Patients

by Kiran Kumar Paidipati, Hyndhavi Komaragiri and Christophe Chesneau

Int. J. Environ. Res. Public Health 2021, 18(15), 7842; https://doi.org/10.3390/ijerph18157842 - 24 Jul 2021

Cited by 5 | Viewed by 3511

Abstract

Diet management or caloric restriction for diabetes mellitus patients is essential in order to reduce the disease’s burden. Mathematical programming problems can help in this regard; they have a central role in optimal diet management and in the nutritional balance of food recipes. The present study employed linear optimization models such as linear, pre-emptive, and non-pre-emptive goal programming problems (LPP, PGP and NPGP) to minimize the deviations of over and under achievements of specific nutrients for optimal selection of food menus with various energy (calories) levels. Sixty-two food recipes are considered, all selected because of being commonly available for the Indian population and developed dietary intake for meal planning through optimization models. The results suggest that a variety of Indian food recipes with low glycemic values can be chosen to assist the varying glucose levels (>200 mg/dL) of Indian diabetes patients. Full article

(This article belongs to the Special Issue Nonparametric Statistics and Machine Learning with Applications in Health Studies)

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14 pages, 2107 KiB

Open AccessArticle

An Analysis of the Areas Occupied by Vessels in the Ocular Surface of Diabetic Patients: An Application of a Nonparametric Tilted Additive Model

by Farzaneh Boroumand, Mohammad Taghi Shakeri, Touka Banaee, Hamidreza Pourreza and Hassan Doosti

Int. J. Environ. Res. Public Health 2021, 18(7), 3735; https://doi.org/10.3390/ijerph18073735 - 2 Apr 2021

Viewed by 2454

Abstract

(1) Background: As diabetes melllitus (DM) can affect the microvasculature, this study evaluates different clinical parameters and the vascular density of ocular surface microvasculature in diabetic patients. (2) Methods: In this cross-sectional study, red-free conjunctival photographs of diabetic individuals aged 30–60 were taken under defined conditions and analyzed using a Radon transform-based algorithm for vascular segmentation. The Areas Occupied by Vessels (AOV) images of different diameters were calculated. To establish the sum of AOV of different sized vessels. We adopt a novel approach to investigate the association between clinical characteristics as the predictors and AOV as the outcome, that is Tilted Additive Model (TAM). We use a tilted nonparametric regression estimator to estimate the nonlinear effect of predictors on the outcome in the additive setting for the first time. (3) Results: The results show Age (p-value = 0.019) and Mean Arterial Pressure (MAP) have a significant linear effect on AOV (p-value = 0.034). We also find a nonlinear association between Body Mass Index (BMI), daily Urinary Protein Excretion (UPE), Hemoglobin A1C, and Blood Urea Nitrogen (BUN) with AOV. (4) Conclusions: As many predictors do not have a linear relationship with the outcome, we conclude that the TAM will help better elucidate the effect of the different predictors. The highest level of AOV can be seen at Hemoglobin A1C of 9% and AOV increases when the daily UPE exceeds 600 mg. These effects need to be considered in future studies of ocular surface vessels of diabetic patients. Full article

(This article belongs to the Special Issue Nonparametric Statistics and Machine Learning with Applications in Health Studies)

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18 pages, 1878 KiB

Open AccessArticle

Bayesian Spatial Survival Analysis of Duration to Cure among New Smear-Positive Pulmonary Tuberculosis (PTB) Patients in Iran, during 2011–2018

by Eisa Nazar, Hossein Baghishani, Hassan Doosti, Vahid Ghavami, Ehsan Aryan, Mahshid Nasehi, Saeid Sharafi, Habibollah Esmaily and Jamshid Yazdani Charati

Int. J. Environ. Res. Public Health 2021, 18(1), 54; https://doi.org/10.3390/ijerph18010054 - 23 Dec 2020

Cited by 8 | Viewed by 3520

Abstract

Mycobacterium tuberculosis is the causative agent of tuberculosis (TB), and pulmonary TB is the most prevalent form of the disease worldwide. One of the most concrete actions to ensure an effective TB control program is monitoring TB treatment outcomes, particularly duration to cure; but, there is no strong evidence in this respect. Thus, the primary aim of this study was to examine the possible spatial variations of duration to cure and its associated factors in Iran using the Bayesian spatial survival model. All new smear-positive PTB patients have diagnosed from March 2011 to March 2018 were included in the study. Out of 34,744 patients, 27,752 (79.90%) patients cured and 6992 (20.10%) cases were censored. For inferential purposes, the Markov chain Monte Carlo algorithms are applied in a Bayesian framework. According to the Bayesian estimates of the regression parameters in the proposed model, a Bayesian spatial log-logistic model, the variables gender (male vs. female, TR = 1.09), altitude (>750 m vs. ≤750 m, TR = 1.05), bacilli density in initial smear (3+ and 2+ vs. 1–9 Basil & 1+, TR = 1.09 and TR = 1.02, respectively), delayed diagnosis (>3 months vs. <1 month, TR = 1.02), nationality (Iranian vs. other, TR = 1.02), and location (urban vs. rural, TR = 1.02) had a significant influence on prolonging the duration to cure. Indeed, pretreatment weight (TR = 0.99) was substantially associated with shorter duration to cure. In summary, the spatial log-logistic model with convolution prior represented a better performance to analyze the duration to cure of PTB patients. Also, our results provide valuable information on critical determinants of duration to cure. Prolonged duration to cure was observed in provinces with low TB incidence and high average altitude as well. Accordingly, it is essential to pay a special attention to such provinces and monitor them carefully to reduce the duration to cure while maintaining a focus on high-risk provinces in terms of TB prevalence. Full article

(This article belongs to the Special Issue Nonparametric Statistics and Machine Learning with Applications in Health Studies)

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9 pages, 455 KiB

Open AccessArticle

Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography

by Parastoo Golpour, Majid Ghayour-Mobarhan, Azadeh Saki, Habibollah Esmaily, Ali Taghipour, Mohammad Tajfard, Hamideh Ghazizadeh, Mohsen Moohebati and Gordon A. Ferns

Int. J. Environ. Res. Public Health 2020, 17(18), 6449; https://doi.org/10.3390/ijerph17186449 - 4 Sep 2020

Cited by 45 | Viewed by 4667

Abstract

(1) Background: Coronary angiography is considered to be the most reliable method for the diagnosis of cardiovascular disease. However, angiography is an invasive procedure that carries a risk of complications; hence, it would be preferable for an appropriate method to be applied to determine the necessity for angiography. The objective of this study was to compare support vector machine, naïve Bayes and logistic regressions to determine the diagnostic factors that can predict the need for coronary angiography. These models are machine learning algorithms. Machine learning is considered to be a branch of artificial intelligence. Its aims are to design and develop algorithms that allow computers to improve their performance on data analysis and decision making. The process involves the analysis of past experiences to find practical and helpful regularities and patterns, which may also be overlooked by a human. (2) Materials and Methods: This cross-sectional study was performed on 1187 candidates for angiography referred to Ghaem Hospital, Mashhad, Iran from 2011 to 2012. A logistic regression, naive Bayes and support vector machine were applied to determine whether they could predict the results of angiography. Afterwards, the sensitivity, specificity, positive and negative predictive values, AUC (area under the curve) and accuracy of all three models were computed in order to compare them. All analyses were performed using R 3.4.3 software (R Core Team; Auckland, New Zealand) with the help of other software packages including receiver operating characteristic (ROC), caret, e1071 and rminer. (3) Results: The area under the curve for logistic regression, naïve Bayes and support vector machine were similar—0.76, 0.74 and 0.75, respectively. Thus, in terms of the model parsimony and simplicity of application, the naïve Bayes model with three variables had the best performance in comparison with the logistic regression model with seven variables and support vector machine with six variables. (4) Conclusions: Gender, age and fasting blood glucose (FBG) were found to be the most important factors to predict the result of coronary angiography. The naïve Bayes model performed well using these three variables alone, and they are considered important variables for the other two models as well. According to an acceptable prediction of the models, they can be used as pragmatic, cost-effective and valuable methods that support physicians in decision making. Full article

(This article belongs to the Special Issue Nonparametric Statistics and Machine Learning with Applications in Health Studies)

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