Prediction of COVID-19 patients' participation in financing informal care using machine learning methods: willingness to pay and willingness to accept approaches.

BACKGROUND: Informal care plays an essential role in managing the COVID-19 pandemic. Expanding health insurance packages that reimburse caregivers' services through cost-sharing policies could increase financial resources. Predicting payers' willingness to contribute financially accurately is essential for implementing such a policy. This study aimed to identify the key variables related to WTP/WTA of COVID-19 patients for informal care in Sanandaj city, Iran. METHODS: This cross-sectional study involved 425 COVID-19 patients in Sanandaj city, Iran, and 23 potential risk factors. We compared the performance of three classifiers based on total accuracy, specificity, sensitivity, negative likelihood ratio, and positive likelihood ratio. RESULTS: Findings showed that the average total accuracy of all models was over 70%. Random trees had the most incredible total accuracy for both patient WTA and patient WTP(0.95 and 0.92). Also, the most significant specificity (0.93 and 0.94), sensitivity (0.91 and 0.87), and the lowest negative likelihood ratio (0.193 and 0.19) belonged to this model. According to the random tree model, the most critical factor in patient WTA were patient difficulty in personal activities, dependency on the caregiver, number of caregivers, patient employment, and education, caregiver employment and patient hospitalization history. Also, for WTP were history of COVID-19 death of patient's relatives, and patient employment status. CONCLUSION: Implementing of a more flexible work schedule, encouraging employer to support employee to provide informal care, implementing educational programs to increase patients' efficacy, and providing accurate information could lead to increased patients' willingness to contribute and finally promote health outcomes in the population.

GADNN: a revolutionary hybrid deep learning neural network for age and sex determination utilizing cone beam computed tomography images of maxillary and frontal sinuses.

INTRODUCTION: The determination of identity factors such as age and sex has gained significance in both criminal and civil cases. Paranasal sinuses like frontal and maxillary sinuses, are resistant to trauma and can aid profiling. We developed a deep learning (DL) model optimized by an evolutionary algorithm (genetic algorithm/GA) to determine sex and age using paranasal sinus parameters based on cone-beam computed tomography (CBCT). METHODS: Two hundred and forty CBCT images (including 129 females and 111 males, aged 18-52) were included in this study. CBCT images were captured using the Newtom3G device with specific exposure parameters. These images were then analyzed in ITK-SNAP 3.6.0 beta software to extract four paranasal sinus parameters: height, width, length, and volume for both the frontal and maxillary sinuses. A hybrid model, Genetic Algorithm-Deep Neural Network (GADNN), was proposed for feature selection and classification. Traditional statistical methods and machine learning models, including logistic regression (LR), random forest (RF), multilayer perceptron neural network (MLP), and deep learning (DL) were evaluated for their performance. The synthetic minority oversampling technique was used to deal with the unbalanced data. RESULTS: GADNN showed superior accuracy in both sex determination (accuracy of 86%) and age determination (accuracy of 68%), outperforming other models. Also, DL and RF were the second and third superior methods in sex determination (accuracy of 78% and 71% respectively) and age determination (accuracy of 92% and 57%). CONCLUSIONS: The study introduces a novel approach combining DL and GA to enhance sex determination and age determination accuracy. The potential of DL in forensic dentistry is highlighted, demonstrating its efficiency in improving accuracy for sex determination and age determination. The study contributes to the burgeoning field of DL in dentistry and forensic sciences.

Identification of Prognostic Biomarkers for Breast Cancer Metastasis Using Penalized Additive Hazards Regression Model.

Background: Breast cancer (BC) has been reported as one of the most common cancers diagnosed in females throughout the world. Survival rate of BC patients is affected by metastasis. So, exploring its underlying mechanisms and identifying related biomarkers to monitor BC relapse/recurrence using new statistical methods is essential. This study investigated the high-dimensional gene-expression profiles of BC patients using penalized additive hazards regression models. Methods: A publicly available dataset related to the time to metastasis in BC patients (GSE2034) was used. There was information of 22 283 genes expression profiles related to 286 BC patients. Penalized additive hazards regression models with different penalties, including LASSO, SCAD, SICA, MCP and Elastic net were used to identify metastasis related genes. Results: Five regression models with penalties were applied in the additive hazards model and jointly found 9 genes including SNU13, CLINT1, MAPK9, ABCC5, NKX3-1, NCOR2, COL2A1, and ZNF219. According the median of the prognostic index calculated using the regression coefficients of the penalized additive hazards model, the patients were labeled as high/low risk groups. A significant difference was detected in the survival curves of the identified groups. The selected genes were examined using validation data and were significantly associated with the hazard of metastasis. Conclusion: This study showed that MAPK9, NKX3-1, NCOR1, ABCC5, and CD44 are the potential recurrence and metastatic predictors in breast cancer and can be taken into account as candidates for further research in tumorigenesis, invasion, metastasis, and epithelial-mesenchymal transition of breast cancer.

Effects of Supplemental Chromium Nanoparticles on IFN-γ expression of Heat Stress Broilers.

The aim of present study was to investigate the beneficial effect of chromium (III) picolinate (CrPic) and chromium (III) picolinate nanoparticles (NCrPic) addition on growth performance, stress-related hormonal changes, and serum levels of various immunity biomarkers, as well as the gene expression of IFN-γ in broilers exposed to heat stress conditions. Treatments included T1 which received the basal diet with no feed additive; T2 exposed to heat stress; T3, T4, and T5 containing 500, 1000, and 1500 ppb CrPic; as well as T6, T7, and T8 containing 500, 1000, and 1500 ppb NCrPic, respectively. After 2 weeks from CrPic and NCrPic supplementation, IFN-γ mRNA expression was assayed using the RT-PCR technique. The results showed that the lower body weight, daily weight gain, daily feed intake by heat stress, and the feed conversion ratio were recovered remarkably by CrPic and NCrPic supplements. The stress-elevated levels of cortisol and immunoglobulin were reduced significantly using CrPic and NCrPic supplementation (P ≤ 0.05). The gene expression profile showed that the upregulated expression of IFN-γ was regulated by the addition of CrPic and NCrPic, in particular, to the diet; however, a full downregulation of IFN-γ expression was observed after week 2 of NCrPic supplementation. In conclusion, the results indicated that nanoparticle supplementation could be effective in reducing heat stress-induced detrimental alterations, thereby attributing to substantial changes to the immune system, including IFN-γ expression.

Regularized Weighted Nonparametric Likelihood Approach for High-Dimension Sparse Subdistribution Hazards Model for Competing Risk Data.

Variable selection and penalized regression models in high-dimension settings have become an increasingly important topic in many disciplines. For instance, omics data are generated in biomedical researches that may be associated with survival of patients and suggest insights into disease dynamics to identify patients with worse prognosis and to improve the therapy. Analysis of high-dimensional time-to-event data in the presence of competing risks requires special modeling techniques. So far, some attempts have been made to variable selection in low- and high-dimension competing risk setting using partial likelihood-based procedures. In this paper, a weighted likelihood-based penalized approach is extended for direct variable selection under the subdistribution hazards model for high-dimensional competing risk data. The proposed method which considers a larger class of semiparametric regression models for the subdistribution allows for taking into account time-varying effects and is of particular importance, because the proportional hazards assumption may not be valid in general, especially in the high-dimension setting. Also, this model relaxes from the constraint of the ability to simultaneously model multiple cumulative incidence functions using the Fine and Gray approach. The performance/effectiveness of several penalties including minimax concave penalty (MCP); adaptive LASSO and smoothly clipped absolute deviation (SCAD) as well as their L2 counterparts were investigated through simulation studies in terms of sensitivity/specificity. The results revealed that sensitivity of all penalties were comparable, but the MCP and MCP-L2 penalties outperformed the other methods in term of selecting less noninformative variables. The practical use of the model was investigated through the analysis of genomic competing risk data obtained from patients with bladder cancer and six genes of CDC20, NCF2, SMARCAD1, RTN4, ETFDH, and SON were identified using all the methods and were significantly correlated with the subdistribution.

Prediction of Multiple sclerosis disease using machine learning classifiers: a comparative study.

INTRODUCTION: Hamedan Province is one of Iran's high-risk regions for Multiple Sclerosis (MS). Early diagnosis of MS based on an accurate system can control the disease. The aim of this study was to compare the performance of four machine learning techniques with traditional methods for predicting MS patients. METHODS: The study used information regarding 200 patients through a case-control study conducted in Hamadan, Western Iran, from 2013 to 2015. The performance of six classifiers was used to compare their performance in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), negative likelihood ratio (LR-) and total accuracy. RESULTS: Random Forest (RF) model illustrated better performance among other models in both scenarios. It had greater specificity (0.67), PPV (0.68) and total accuracy (0.68). The most influential diagnostic factors for MS were age, birth season and gender. CONCLUSIONS: Our findings showed that despite all the six methods performed almost similarly, the RF model performed slightly better in terms of different criteria in prediction accuracy. Accordingly, this approach is an effective classifier for predicting MS in the early stage and control the disease.

Investigating climatic changes of the wind regime over Western Iran.

OBJECTIVE: The aim of the present study was to reveal changes in the wind regime by investigating wind-speed data from meteorological stations in western Iran and comparing them in the last three decades (1986-2015). RESULTS: Two main groups of daily cycles were identified; one group with a single peak and one group with two or more peaks. Using spectral decomposition technique, it was revealed that the heterogeneity observed in the area in terms of altitude and topography results in differences in the density of the spectra with similar frequencies. Two main daily cycles were also identified for each station. Although there were low frequencies, the intensity of the waves at the examined stations was the consequence of the interaction between the frequency, period, and distribution space. By evaluating harmonics in the area, it was revealed that the variance of the first harmonic is maximized in the south and southwest, while the variance of the second harmonic is maximized in the north and northwest. The positive value ​​of the trend in the first harmonic indicated that the trend of the variance for the first harmonic has increased in the central and eastern parts and has decreased in the northern and western parts.

Random effect exponentiated-exponential geometric model for clustered/longitudinal zero-inflated count data.

For count responses, there are situations in biomedical and sociological applications in which extra zeroes occur. Modeling correlated (e.g. repeated measures and clustered) zero-inflated count data includes special challenges because the correlation between measurements for a subject or a cluster needs to be taken into account. Moreover, zero-inflated count data are often faced with over/under dispersion problem. In this paper, we propose a random effect model for repeated measurements or clustered data with over/under dispersed response called random effect zero-inflated exponentiated-exponential geometric regression model. The proposed method was illustrated through real examples. The performance of the model and asymptotical properties of the estimations were investigated using simulation studies.

A comparison of three data mining time series models in prediction of monthly brucellosis surveillance data.

The early and accurately detection of brucellosis incidence change is of great importance for implementing brucellosis prevention strategic health planning. The present study investigated and compared the performance of the three data mining techniques, random forest (RF), support vector machine (SVM) and multivariate adaptive regression splines (MARSs), in time series modelling and predicting of monthly brucellosis data from 2005 (March/April) to 2017 (February/March) extracted from a national public health surveillance system in Hamadan located in west of Iran. The performances were compared based on the root mean square errors, mean absolute errors, determination coefficient (R2 ) and intraclass correlation coefficient criteria. Results indicated that the RF model outperformed the SVM and MARS models in modeling used data and it can be utilized successfully utilized to diagnose the behaviour of brucellosis over time. Further research with application of such novel time series models in practice provides the most appropriate method in the control and prevention of future outbreaks for the epidemiologist.

Comparative evaluation of time series models for predicting influenza outbreaks: application of influenza-like illness data from sentinel sites of healthcare centers in Iran.

OBJECTIVE: Forecasting the time of future outbreaks would minimize the impact of diseases by taking preventive steps including public health messaging and raising awareness of clinicians for timely treatment and diagnosis. The present study investigated the accuracy of support vector machine, artificial neural-network, and random-forest time series models in influenza like illness (ILI) modeling and outbreaks detection. The models were applied to a data set of weekly ILI frequencies in Iran. The root mean square errors (RMSE), mean absolute errors (MAE), and intra-class correlation coefficient (ICC) statistics were employed as evaluation criteria. RESULTS: It was indicated that the random-forest time series model outperformed other three methods in modeling weekly ILI frequencies (RMSE = 22.78, MAE = 14.99 and ICC = 0.88 for the test set). In addition neural-network was better in outbreaks detection with total accuracy of 0.889 for the test set. The results showed that the used time series models had promising performances suggesting they could be effectively applied for predicting weekly ILI frequencies and outbreaks.

Landmark Prediction of Survival for Breast Cancer Patients: A Case Study in Tehran, Iran.

BACKGROUND: Breast cancer is the first non-cutaneous malignancy in women and the second cause of death due to cancer all over the world. There are situations where researchers are interested in dynamic prediction of survival of patients where traditional models might fail to achieve this goal. We aimed to use a dynamic prediction model in analyzing survival of breast cancer patients. METHODS: We used a data set originates from a retrospective cohort (registry-based) study conducted in 2014 in Tehran, Iran, information of 550 patients were available analyzed. A method of landmarking was utilized for dynamic prediction of survival of the patients. The criteria of time-dependent area under the curve and prediction error curve were used to evaluate the performance of the model. RESULTS: An index of risk score (prognostic index) was calculated according to the available covariates based on Cox proportional hazards. Therefore, hazard of dying for a high-risk patient with breast cancer within the next five years was 2.69 to 3.04 times of that for a low-risk patient. The value of the dynamic C-index was 0.89 using prognostic index as covariate. CONCLUSION: Generally, the landmark model showed promising performance in predicting survival or probability of dying for breast cancer patients in this study in a predefined window. Therefore, this model can be used in other studies as a useful model for investigating the survival of breast cancer patients.

Multistate recursively imputed survival trees for time-to-event data analysis: an application to AIDS and mortality post-HIV infection data.

BACKGROUND: This study aimed to introduce recursively imputed survival trees into multistate survival models (MSRIST) to analyze these types of data and to identify the prognostic factors influencing the disease progression in patients with intermediate events. The proposed method is fully nonparametric and can be used for estimating transition probabilities. METHODS: A general algorithm was provided for analyzing multi-state data with a focus on the illness-death and progressive multi-state models. The model considered both beyond Markov and Non-Markov settings. We also proposed a multi-state random survival method (MSRSF) and compared their performance with the classical multi-state Cox model. We applied the proposed method to a dataset related to HIV/AIDS patients based on a retrospective cohort study extracted in Tehran from April 2004 to March 2014 consist of 2473 HIV-infected patients. RESULTS: The results showed that MSRIST outperformed the classical multistate method using Cox Model and MSRSF in terms of integrated Brier score and concordance index over 500 repetitions. We also identified a set of important risk factors as well as their interactions on different states of HIV and AIDS progression. CONCLUSIONS: There are different strategies for modelling the intermediate event. We adapted two newly developed data mining technique (RSF and RIST) for multistate models (MSRSF and MSRIST) to identify important risk factors in different stages of the diseases. The methods can capture any complex relationship between variables and can be used as a useful tool for identifying important risk factors in different states of this disease.

Investigating the effect of climatic parameters on mental disorder admissions.

The main objective of this study was to evaluate the role of climatic parameters and phenomena including the monthly number of dusty/rainy/snowy/foggy days, cloudiness (Okta), horizontal visibility, and barometric pressure (millibar) on major depressive disorder, bipolar, schizophrenia, and schizoaffective admissions. The monthly data related to the number of admissions in Farshchian hospital and climatic parameters from March 2005 to March 2017 were extracted. Random forest regression and dynamic negative binomial regression were used to examine the relationship between variables; the statistical significance was considered as 0.05. The number of dusty/rainy/snowy/foggy days, cloudiness, and the number of days with vision less than 2 km had a significant positive relationship with admissions due to schizophrenia (p < 0.05). Barometric pressure had a negative effect on schizophrenia admissions (p < 0.001). The number of dusty/rainy/snowy/foggy days and cloudiness had a significant effect on schizoaffective admissions (p < 0.05). Bipolar admissions were negatively associated with rainy days and positively associated with dusty days and cloudiness (p < 0.05). The number of rainy/dusty/snowy days and cloudiness had a positive significant effect on major depressive disorder admissions. The results of the present study confirmed the importance of climatic parameter variability for major depressive disorder, bipolar, schizophrenia, and schizoaffective admissions.

Prediction of Kidney Graft Rejection Using Artificial Neural Network.

OBJECTIVES: Kidney transplantation is the best renal replacement therapy for patients with end-stage renal disease. Several studies have attempted to identify predisposing factors of graft rejection; however, the results have been inconsistent. We aimed to identify prognostic factors associated with kidney transplant rejection using the artificial neural network (ANN) approach and to compare the results with those obtained by logistic regression (LR). METHODS: The study used information regarding 378 patients who had undergone kidney transplantation from a retrospective study conducted in Hamadan, Western Iran, from 1994 to 2011. ANN was used to identify potential important risk factors for chronic nonreversible graft rejection. RESULTS: Recipients' age, creatinine level, cold ischemic time, and hemoglobin level at discharge were identified as the most important prognostic factors by ANN. The ANN model showed higher total accuracy (0.75 vs. 0.55 for LR), and the area under the ROC curve (0.88 vs. 0.75 for LR) was better than that obtained with LR. CONCLUSIONS: The results of this study indicate that the ANN model outperformed LR in the prediction of kidney transplantation failure. Therefore, this approach is a promising classifier for predicting graft failure to improve patients' survival and quality of life, and it should be further investigated for the prediction of other clinical outcomes.

Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods.

OBJECTIVES: Preterm birth (PTB) is a leading cause of neonatal death and the second biggest cause of death in children under five years of age. The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods. METHODS: This cross-sectional study was conducted on 4,415 pregnant women in Tehran, Iran, from July 6-21, 2015. Data were collected by a researcher-developed questionnaire through interviews with mothers and review of their medical records. To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used. RESULTS: The PTB rate was 5.5% in this study. The logistic regression outperformed the decision tree for the classification of PTB based on risk factors. Logistic regression showed that multiple pregnancies, mothers with preeclampsia, and those who conceived with assisted reproductive technology had an increased risk for PTB (p < 0.05). CONCLUSION: Identifying and training mothers at risk as well as improving prenatal care may reduce the PTB rate. We also recommend that statisticians utilize the logistic regression model for the classification of risk groups for PTB.

Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests.

BACKGROUND: Kidney transplantation is the best alternative treatment for end-stage renal disease. Several studies have been devoted to investigate predisposing factors of graft rejection. However, there is inconsistency between the results. The objective of the present study was to utilize an intuitive and robust approach for variable selection, random survival forests (RSF), and to identify important risk factors in kidney transplantation patients. METHODS: The data set included 378 patients with kidney transplantation obtained through a historical cohort study in Hamadan, western Iran, from 1994 to 2011. The event of interest was chronic nonreversible graft rejection and the duration between kidney transplantation and rejection was considered as the survival time. RSF method was used to identify important risk factors for survival of the patients among the potential predictors of graft rejection. RESULTS: The mean survival time was 7.35±4.62 yr. Thirty-seven episodes of rejection were occurred. The most important predictors of survival were cold ischemic time, recipient's age, creatinine level at discharge, donors' age and duration of hospitalization. RSF method predicted survival better than the conventional Cox-proportional hazards model (out-of-bag C-index of 0.965 for RSF vs. 0.766 for Cox model and integrated Brier score of 0.081 for RSF vs. 0.088 for Cox model). CONCLUSION: A RSF model in the kidney transplantation patients outperformed traditional Cox-proportional hazard model. RSF is a promising method that may serve as a more intuitive approach to identify important risk factors for graft rejection.

High-dimensional additive hazards regression for oral squamous cell carcinoma using microarray data: a comparative study.

Microarray technology results in high-dimensional and low-sample size data sets. Therefore, fitting sparse models is substantial because only a small number of influential genes can reliably be identified. A number of variable selection approaches have been proposed for high-dimensional time-to-event data based on Cox proportional hazards where censoring is present. The present study applied three sparse variable selection techniques of Lasso, smoothly clipped absolute deviation and the smooth integration of counting, and absolute deviation for gene expression survival time data using the additive risk model which is adopted when the absolute effects of multiple predictors on the hazard function are of interest. The performances of used techniques were evaluated by time dependent ROC curve and bootstrap .632+ prediction error curves. The selected genes by all methods were highly significant (P < 0.001). The Lasso showed maximum median of area under ROC curve over time (0.95) and smoothly clipped absolute deviation showed the lowest prediction error (0.105). It was observed that the selected genes by all methods improved the prediction of purely clinical model indicating the valuable information containing in the microarray features. So it was concluded that used approaches can satisfactorily predict survival based on selected gene expression measurements.

Real-data comparison of data mining methods in prediction of diabetes in iran.

OBJECTIVES: Diabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-mean, and random forests) to classify persons with and without diabetes. METHODS: The data set used in this study included 6,500 subjects from the Iranian national non-communicable diseases risk factors surveillance obtained through a cross-sectional survey. The obtained sample was based on cluster sampling of the Iran population which was conducted in 2005-2009 to assess the prevalence of major non-communicable disease risk factors. Ten risk factors that are commonly associated with diabetes were selected to compare the performance of six classifiers in terms of sensitivity, specificity, total accuracy, and area under the receiver operating characteristic (ROC) curve criteria. RESULTS: Support vector machines showed the highest total accuracy (0.986) as well as area under the ROC (0.979). Also, this method showed high specificity (1.000) and sensitivity (0.820). All other methods produced total accuracy of more than 85%, but for all methods, the sensitivity values were very low (less than 0.350). CONCLUSIONS: The results of this study indicate that, in terms of sensitivity, specificity, and overall classification accuracy, the support vector machine model ranks first among all the classifiers tested in the prediction of diabetes. Therefore, this approach is a promising classifier for predicting diabetes, and it should be further investigated for the prediction of other diseases.