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Article: Developing an intelligent prediction system for successful aging based on artificial neural networks.

Nopour, Raoof / Kazemi-Arpanahi, Hadi

International journal of preventive medicine

2024 Volume 15, Page(s) 10

Abstract: Background: Due to the growing number of disabilities in elderly, Attention to this period of life is essential to be considered. Few studies focused on the physical, mental, disabilities, and disorders affecting the quality of life in elderly people. ... ...

Abstract	Background: Due to the growing number of disabilities in elderly, Attention to this period of life is essential to be considered. Few studies focused on the physical, mental, disabilities, and disorders affecting the quality of life in elderly people. SA Methods: This study was performed on 1156 SA and non-SA cases. We applied statistical feature reduction method to obtain the best factors predicting the SA. Two models of ANNs with 5, 10, 15, and 20 neurons in hidden layers were used for model construction. Finally, the best ANN configuration was obtained for predicting the SA using sensitivity, specificity, accuracy, and cross-entropy loss function. Results: The study showed that 25 factors correlated with SA at the statistical level of Conclusions: Developing the CDSS for predicting SA has crucial role to effectively inform geriatrics and health care policymakers decision making.
Language	English
Publishing date	2024-02-29
Publishing country	Iran
Document type	Journal Article
ZDB-ID	2574680-7
ISSN	2008-8213 ; 2008-7802
ISSN (online)	2008-8213
ISSN	2008-7802
DOI	10.4103/ijpvm.ijpvm_47_23
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks.

Nopour, Raoof / Shanbezadeh, Mostafa / Kazemi-Arpanahi, Hadi

Journal of education and health promotion

2023 Volume 12, Page(s) 16

Abstract: Background: Accurately predicting the intubation risk in COVID-19 patients at the admission time is critical to optimal use of limited hospital resources, providing customized and evidence-based treatments, and improving the quality of delivered medical ...

Abstract	Background: Accurately predicting the intubation risk in COVID-19 patients at the admission time is critical to optimal use of limited hospital resources, providing customized and evidence-based treatments, and improving the quality of delivered medical care services. This study aimed to design a statistical algorithm to select the best features influencing intubation prediction in coronavirus disease 2019 (COVID-19) hospitalized patients. Then, using selected features, multiple artificial neural network (ANN) configurations were developed to predict intubation risk. Material and methods: In this retrospective single-center study, a dataset containing 482 COVID-19 patients who were hospitalized between February 9, 2020 and July 20, 2021 was used. First, the Phi correlation coefficient method was performed for selecting the most important features affecting COVID-19 patients' intubation. Then, the different configurations of ANN were developed. Finally, the performance of ANN configurations was assessed using several evaluation metrics, and the best structure was determined for predicting intubation requirements among hospitalized COVID-19 patients. Results: The ANN models were developed based on 18 validated features. The results indicated that the best performance belongs to the 18-20-1 ANN configuration with positive predictive value (PPV) = 0.907, negative predictive value (NPV) = 0.941, sensitivity = 0.898, specificity = 0.951, and area under curve (AUC) = 0.906. Conclusions: The results demonstrate the effectiveness of the ANN models for timely and reliable prediction of intubation risk in COVID-19 hospitalized patients. Our models can inform clinicians and those involved in policymaking and decision making for prioritizing restricted mechanical ventilation and other related resources for critically COVID-19 patients.
Language	English
Publishing date	2023-01-31
Publishing country	India
Document type	Journal Article
ZDB-ID	2715449-X
ISSN	2319-6440 ; 2277-9531
ISSN (online)	2319-6440
ISSN	2277-9531
DOI	10.4103/jehp.jehp_20_22
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Article ; Online: Using an adaptive network-based fuzzy inference system for prediction of successful aging: a comparison with common machine learning algorithms.

Yazdani, Azita / Shanbehzadeh, Mostafa / Kazemi-Arpanahi, Hadi

BMC medical informatics and decision making

2023 Volume 23, Issue 1, Page(s) 229

Abstract: Introduction: The global society is currently facing a rise in the elderly population. The concept of successful aging (SA) appeared in the gerontological literature to overcome the challenges and problems of population aging. SA is a subjective and ... ...

Abstract	Introduction: The global society is currently facing a rise in the elderly population. The concept of successful aging (SA) appeared in the gerontological literature to overcome the challenges and problems of population aging. SA is a subjective and multidimensional concept with many ambiguities regarding its meaning or measuring. This study aimed to propose an intelligent predictive model to predict SA. Methods: In this retrospective study, the data of 784 elderly people were used to develop and validate machine learning (ML) methods. Data pre-processing was first performed. First, an adaptive neuro-fuzzy inference system (ANFIS) was proposed to predict SA. Then, the predictive performance of the proposed model was compared with three ML algorithms, including multilayer perceptron (MLP) neural network, support vector machine (SVM), and random forest (RF) based on accuracy, sensitivity, precision, and F-score metrics. Results: The findings indicated that the ANFIS model with gauss2mf built-in membership function (MF) outperformed the other models with accuracy, sensitivity, precision, and F-score of 91.57%, 95.18%, 92.31%, and 92.94%, respectively. Conclusions: The predictive performance of ANFIS is more efficient than the other ML models in SA prediction. The development of a decision support system (DSS) using our prediction model can provide healthcare administrators and policymakers with a reliable and responsive tool to improve elderly outcomes.
MeSH term(s)	Aged ; Humans ; Retrospective Studies ; Fuzzy Logic ; Algorithms ; Machine Learning ; Aging
Language	English
Publishing date	2023-10-19
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2046490-3
ISSN	1472-6947 ; 1472-6947
ISSN (online)	1472-6947
ISSN	1472-6947
DOI	10.1186/s12911-023-02335-9
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Article: Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study.

Nopour, Raoof / Shanbehzadeh, Mostafa / Kazemi-Arpanahi, Hadi

Medical journal of the Islamic Republic of Iran

2022 Volume 36, Page(s) 30

Abstract: Background: ...

Abstract	Background:
Language	English
Publishing date	2022-04-04
Publishing country	Iran
Document type	Journal Article
ZDB-ID	1095990-7
ISSN	1016-1430
ISSN	1016-1430
DOI	10.47176/mjiri.36.30
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Article: Factors influencing quality of life among the elderly: An approach using logistic regression.

Ahmadi, Maryam / Kazemi-Arpanahi, Hadi / Nopour, Raoof / Shanbehzadeh, Mostafa

Journal of education and health promotion

2023 Volume 12, Page(s) 215

Abstract: Background: Improving the physical, psychological, and social factors in the elderly significantly increases the QoL: Materials and methods: In this study, 980 samples related to the elderly with favorable and unfavorable QoL were investigated. The ... ...

Abstract	Background: Improving the physical, psychological, and social factors in the elderly significantly increases the QoL Materials and methods: In this study, 980 samples related to the elderly with favorable and unfavorable QoL were investigated. The elderly's QoL was investigated using a qualitative and self-assessment questionnaire that measured the QoL among them by five Likert spectrum and independent factors. The Chi-square test and eta coefficient were used to determine the relationship between each predicting factor of the elderly's QoL in SPSS V 25 software. Finally, we used the Enter and Forward LR methods to determine the correlation of influential factors in the presence of other variables. Results: The study showed that 20 variables gained a significant relationship with the quality of life of the elderly at Conclusion: Attempts to identify and modify the important factors affecting the elderly's QoL have a significant role in improving the QoL and life satisfaction in this age group people. This study showed that the statistical methods have a pleasant capability to discover the factors associated with the elderly's QoL with high performance in this regard.
Language	English
Publishing date	2023-06-30
Publishing country	India
Document type	Journal Article
ZDB-ID	2715449-X
ISSN	2319-6440 ; 2277-9531
ISSN (online)	2319-6440
ISSN	2277-9531
DOI	10.4103/jehp.jehp_13_23
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Article ; Online: Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study.

Afrash, Mohammad Reza / Mirbagheri, Esmat / Mashoufi, Mehrnaz / Kazemi-Arpanahi, Hadi

BMC medical informatics and decision making

2023 Volume 23, Issue 1, Page(s) 54

Abstract: Background: Gastric cancer is the most common malignant tumor worldwide and a leading cause of cancer deaths. This neoplasm has a poor prognosis and heterogeneous outcomes. Survivability prediction may help select the best treatment plan based on an ... ...

Abstract	Background: Gastric cancer is the most common malignant tumor worldwide and a leading cause of cancer deaths. This neoplasm has a poor prognosis and heterogeneous outcomes. Survivability prediction may help select the best treatment plan based on an individual's prognosis. Numerous clinical and pathological features are generally used in predicting gastric cancer survival, and their influence on the survival of this cancer has not been fully elucidated. Moreover, the five-year survivability prognosis performances of feature selection methods with machine learning (ML) classifiers for gastric cancer have not been fully benchmarked. Therefore, we adopted several well-known feature selection methods and ML classifiers together to determine the best-paired feature selection-classifier for this purpose. Methods: This was a retrospective study on a dataset of 974 patients diagnosed with gastric cancer in the Ayatollah Talleghani Hospital, Abadan, Iran. First, four feature selection algorithms, including Relief, Boruta, least absolute shrinkage and selection operator (LASSO), and minimum redundancy maximum relevance (mRMR) were used to select a set of relevant features that are very informative for five-year survival prediction in gastric cancer patients. Then, each feature set was fed to three classifiers: XG Boost (XGB), hist gradient boosting (HGB), and support vector machine (SVM) to develop predictive models. Finally, paired feature selection-classifier methods were evaluated to select the best-paired method using the area under the curve (AUC), accuracy, sensitivity, specificity, and f1-score metrics. Results: The LASSO feature selection algorithm combined with the XG Boost classifier achieved an accuracy of 89.10%, a specificity of 87.15%, a sensitivity of 89.42%, an AUC of 89.37%, and an f1-score of 90.8%. Tumor stage, history of other cancers, lymphatic invasion, tumor site, type of treatment, body weight, histological type, and addiction were identified as the most significant factors affecting gastric cancer survival. Conclusions: This study proved the worth of the paired feature selection-classifier to identify the best path that could augment the five-year survival prediction in gastric cancer patients. Our results were better than those of previous studies, both in terms of the time required to form the models and the performance measurement criteria of the algorithms. These findings may be very promising and can, therefore, inform clinical decision-making and shed light on future studies.
MeSH term(s)	Humans ; Stomach Neoplasms ; Prognosis ; Retrospective Studies ; Algorithms ; Machine Learning
Language	English
Publishing date	2023-04-06
Publishing country	England
Document type	Journal Article
ZDB-ID	2046490-3
ISSN	1472-6947 ; 1472-6947
ISSN (online)	1472-6947
ISSN	1472-6947
DOI	10.1186/s12911-023-02154-y
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Article: Development of minimal basic data set to report COVID-19.

Shanbehzadeh, Mostafa / Kazemi-Arpanahi, Hadi

Medical journal of the Islamic Republic of Iran

2020 Volume 34, Page(s) 111

Abstract: Background: ...

Abstract	Background:
Language	English
Publishing date	2020-09-01
Publishing country	Iran
Document type	Journal Article
ZDB-ID	1095990-7
ISSN	1016-1430
ISSN	1016-1430
DOI	10.34171/mjiri.34.111
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bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed tomography severity score data.

Zakariaee, Seyed Salman / Naderi, Negar / Ebrahimi, Mahdi / Kazemi-Arpanahi, Hadi

Scientific reports

2023 Volume 13, Issue 1, Page(s) 11343

Abstract: Since the beginning of the COVID-19 pandemic, new and non-invasive digital technologies such as artificial intelligence (AI) had been introduced for mortality prediction of COVID-19 patients. The prognostic performances of the machine learning (ML)-based ...

Abstract	Since the beginning of the COVID-19 pandemic, new and non-invasive digital technologies such as artificial intelligence (AI) had been introduced for mortality prediction of COVID-19 patients. The prognostic performances of the machine learning (ML)-based models for predicting clinical outcomes of COVID-19 patients had been mainly evaluated using demographics, risk factors, clinical manifestations, and laboratory results. There is a lack of information about the prognostic role of imaging manifestations in combination with demographics, clinical manifestations, and laboratory predictors. The purpose of the present study is to develop an efficient ML prognostic model based on a more comprehensive dataset including chest CT severity score (CT-SS). Fifty-five primary features in six main classes were retrospectively reviewed for 6854 suspected cases. The independence test of Chi-square was used to determine the most important features in the mortality prediction of COVID-19 patients. The most relevant predictors were used to train and test ML algorithms. The predictive models were developed using eight ML algorithms including the J48 decision tree (J48), support vector machine (SVM), multi-layer perceptron (MLP), k-nearest neighbourhood (k-NN), Naïve Bayes (NB), logistic regression (LR), random forest (RF), and eXtreme gradient boosting (XGBoost). The performances of the predictive models were evaluated using accuracy, precision, sensitivity, specificity, and area under the ROC curve (AUC) metrics. After applying the exclusion criteria, a total of 815 positive RT-PCR patients were the final sample size, where 54.85% of the patients were male and the mean age of the study population was 57.22 ± 16.76 years. The RF algorithm with an accuracy of 97.2%, the sensitivity of 100%, a precision of 94.8%, specificity of 94.5%, F1-score of 97.3%, and AUC of 99.9% had the best performance. Other ML algorithms with AUC ranging from 81.2 to 93.9% had also good prediction performances in predicting COVID-19 mortality. Results showed that timely and accurate risk stratification of COVID-19 patients could be performed using ML-based predictive models fed by routine data. The proposed algorithm with the more comprehensive dataset including CT-SS could efficiently predict the mortality of COVID-19 patients. This could lead to promptly targeting high-risk patients on admission, the optimal use of hospital resources, and an increased probability of survival of patients.
MeSH term(s)	Humans ; Adult ; Middle Aged ; Aged ; Artificial Intelligence ; Bayes Theorem ; Pandemics ; Retrospective Studies ; COVID-19/diagnostic imaging ; Tomography, X-Ray Computed ; Algorithms ; Machine Learning
Language	English
Publishing date	2023-07-13
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-38133-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Using decision tree algorithms for estimating ICU admission of COVID-19 patients.

Shanbehzadeh, Mostafa / Nopour, Raoof / Kazemi-Arpanahi, Hadi

Informatics in medicine unlocked

2022 Volume 30, Page(s) 100919

Abstract: Introduction: Coronavirus disease 2019 (COVID-19) outbreak has overwhelmed many healthcare systems worldwide and put them at the edge of collapsing. As intensive care unit (ICU) capacities are limited, deciding on the proper allocation of required ... ...

Abstract	Introduction: Coronavirus disease 2019 (COVID-19) outbreak has overwhelmed many healthcare systems worldwide and put them at the edge of collapsing. As intensive care unit (ICU) capacities are limited, deciding on the proper allocation of required resources is crucial. This study aimed to develop and compare models for early predicting ICU admission in COVID-19 patients at the point of hospital admission. Materials and methods: Using a single-center registry, we studied the records of 512 COVID-19 patients. First, the most important variables were identified using Chi-square test (at p < 0.01) and logistic regression (with odds ratio at P < 0.05). Second, we trained seven decision tree (DT) algorithms (decision stump (DS), Hoeffding tree (HT), LMT, J-48, random forest (RF), random tree (RT) and REP-Tree) using the selected variables. Finally, the models' performance was evaluated. Furthermore, we used an external dataset to validate the prediction models. Results: Using the Chi-square test, 20 important variables were identified. Then, 12 variables were selected for model construction using logistic regression. Comparing the DT methods demonstrated that J-48 (F-score of 0.816 and AUC of 0.845) had the best performance. Also, the J-48 (F-score = 80.9% and AUC = 0.822) gained the best performance in generalizability using the external dataset. Conclusions: The study results demonstrated that DT algorithms can be used to predict ICU admission requirements in COVID-19 patients based on the first time of admission data. Implementing such models has the potential to inform clinicians and managers to adopt the best policy and get prepare during the COVID-19 time-sensitive and resource-constrained situation.
Language	English
Publishing date	2022-03-18
Publishing country	England
Document type	Journal Article
ISSN	2352-9148
ISSN	2352-9148
DOI	10.1016/j.imu.2022.100919
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Article: Design of an artificial neural network to predict mortality among COVID-19 patients.

Shanbehzadeh, Mostafa / Nopour, Raoof / Kazemi-Arpanahi, Hadi

Informatics in medicine unlocked

2022 Volume 31, Page(s) 100983

Abstract: Introduction: The fast pandemic of coronavirus disease 2019 (COVID-19) has challenged clinicians with many uncertainties and ambiguities regarding disease outcomes and complications. To deal with these uncertainties, our study aimed to develop and ... ...

Abstract	Introduction: The fast pandemic of coronavirus disease 2019 (COVID-19) has challenged clinicians with many uncertainties and ambiguities regarding disease outcomes and complications. To deal with these uncertainties, our study aimed to develop and evaluate several artificial neural networks (ANNs) to predict the mortality risk in hospitalized COVID-19 patients. Material and methods: The data of 1710 hospitalized COVID-19 patients were used in this retrospective and developmental study. First, a Chi-square test (P < 0.05), Eta coefficient (η > 0.4), and binary logistics regression (BLR) analysis were performed to determine the factors affecting COVID-19 mortality. Then, using the selected variables, two types of feed-forward (FF) models, including the back-propagation (BP) and distributed time delay (DTD) were trained. The models' performance was assessed using mean squared error (MSE), error histogram (EH), and area under the ROC curve (AUC-ROC) metrics. Results: After applying the univariate and multivariate analysis, 13 variables were selected as important features in predicting COVID-19 mortality at P < 0.05. A comparison of the two ANN architectures using the MSE showed that the BP-ANN (validation error: 0.067, most of the classified samples having 0.049 and 0.05 error rates, and AUC-ROC: 0.888) was the best model. Conclusions: Our findings show the acceptable performance of ANN for predicting the risk of mortality in hospitalized COVID-19 patients. Application of the developed ANN-based CDSS in a real clinical environment will improve patient safety and reduce disease severity and mortality.
Language	English
Publishing date	2022-05-29
Publishing country	England
Document type	Journal Article
ISSN	2352-9148
ISSN	2352-9148
DOI	10.1016/j.imu.2022.100983
Database	MEDical Literature Analysis and Retrieval System OnLINE

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