جستجوی مقالات مرتبط با کلیدواژه "missing data" در نشریات گروه "پزشکی"

This article aims to compare the efficiency of different imputation methods with missing data. In this way we use mean, median, Expected-Maximization (EM), regression imputation(RI) and multiple imputations (MI) to replace missing data.In fact, we employ three proposed combination methods, namely EM imputation with MI imputation (EMMI), EM imputation with regression imputation (EMR), and regression imputation with MIimputation (MI). In this paper, we compare these methods using an example study of Waterborne Container Trade by the US Customs Port (2000-2017) where the methods with different missing percent-ages. Several criteria, are used to compare estimations efficiency, such as mean, Standard Deviation (SD), and Mean Squared Error (MSE). The results show that the efficiency of composite imputation methods in almost all situations, in terms of MSE, RMI imputation method outperforms other methods. Nevertheless, when the missing percentage is small, the EMR imputation method performs better. In terms of the SD criterion, we find that the MI method is better than the other methods, where the RMI method is good when the missing percentage is large. When the missing percentage is in the range (40-50%), the EMR and RMI imputation methods give a better MSE.

Diabetes, a currently threatening disease, has severe consequences for individuals’ health conditions. The present study aimed to investigate the factors affecting the changes in the longitudinal outcome of blood sugar using a three-level analysis with the presence of missing data in diabetic patients.

A total of 526 diabetic patients were followed longitudinally selected from the annual data collected from the rural population monitored by Tonekabon health centers in the North of Iran during 2018-2019 from the Iranian Integrated Health System (SIB) database. In analyzing this longitudinal data, the three-level model (level 1: observation (time), level 2: subject, level 3: health center) was carried out with multiple imputations of possible missing values in longitudinal data.

Results of fitting the three-level model indicated that every unit of change in the body mass index (BMI) significantly increased the fasting blood sugar by an average of 0.5 mg/dl (p=0.024). The impact of level 1 (observations) was insignificant in the three-level model. Still, the random effect of level 3 (healthcare centers) showed a highly significant measure for health centers (14.62, p<0.001).

The BMI reduction, the healthcare centers' socioeconomic status, and the health services provided have potential effects in controlling diabetes.

Chiang Mai’s air pollution has risen to number one in the world for the highest level of fine particulate matter which further exacerbates the damage to human health. Fine particulate matter can enter the human body and blood circulation, destroying organ systems, increasing the risk for chronic disease and cancer, despite not having smoking habits or other morbidities. The Thai government must sort out this issue before it is too late as the whole nation’s health is at risk due to excessive dust levels higher than standard guidelines. Collection of pollution data can help us to come up with solutions and prevent it from turning into a hazardous situation. Unfortunately, pollution data are missing and need to be dealt with before analysis to obtain accurate results.

A new method of imputation for estimating population mean based on a transformed variable has been suggested under simple random sampling without replacement and the uniform nonresponse mechanism. The bias and mean square error of the proposed estimator are investigated up to the first order of approximation. The performance of the proposed estimator is studied via applications to air pollution data in Chiang Mai, Thailand.

The proposed estimator shows the best performance, giving the least bias and mean square error for all levels of sampling fractions. For the results from application the estimated value of sulfur dioxide from Particulate Matter 2.5 (PM2.5), the Percentage Relative Efficiency (PRE) is higher than all existing estimators by at least 16%. For the estimated PM2.5 from PM10 the PRE is higher than all existing estimators by at least 1600%, an extremely significant difference exhibiting similarity to real values.

The proposed imputation technique based on the transformed auxiliary variable can be helpful for imputing missing values and improving the efficiency of the estimators.

A timely diagnosis of Alzheimer’s disease (AD) is crucial to obtain more practical treatments. In this article, a novel approach using Auto-Encoder Neural Networks (AENN) for early detection of AD was proposed.

The proposed method mainly deals with the classification of multimodal data and the imputation of missing data. The data under study involve the MiniMental State Examination, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid data, and personal information. Natural logarithm was used for normalizing the data. The Auto-Encoder Neural Networks was used for imputing missing data. Principal component analysis algorithm was used for reducing dimensionality of data. Support Vector Machine (SVM) was used as classifier. The proposed method was evaluated using Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Then, 10fold crossvalidation was used to audit the detection accuracy of the method.

The effectiveness of the proposed approach was studied under several scenarios considering 705 cases of ADNI database. In three binary classification problems, that is AD vs. normal controls (NCs), mild cognitive impairment (MCI) vs. NC, and MCI vs. AD, we obtained the accuracies of 95.57%, 83.01%, and 78.67%, respectively.

Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.

Due to the thalassemia severe complications, prediction of mortality or patients survival has a great importance in early treatment phases. This study purpose was to predict the mortality rate of patients with thalassemia major and thalassemia intermedia, by the use of the binary logistic regression algorithm and genetic algorithm combination.

This retrospective cohort study was conducted on 909 thalassemia patients by using a questionnaire during 2004-2014. The data of all patients referring to Imam Reza Hospital from 2004 to 2014 have been considered. This study predictive variable is considered to be death or survival of the patient. In this research, we embedded the missing data by the use of the proposed data mining model and MICE algorithm. Totally, 100 patients were excluded from this research, due to the missing or out-of-range data. Death was considered as dependent variable. Also, a predictive model was designed in order to
predict the patient mortality using MATLAB language.

Mean age of the thalassemia patients was 25.7±9.04 years old and at the end of the study death was reported in 185 subjects. Additionally, there were also 26 independent variables. Moreover, the missing variables mean for each patient was 1.8±0.81. The combined predictive model was able to predict the patient survival rate with 94.35% accuracy. In this research, it was found out that 26 independent variables, which were collected from 12 variables were patient mortality predictors. Also, missing data imputation is an important method for increasing the data mining algorithms efficiency.

According to this study results, the use of missing algorithm with the data analysis aid yielded more accurate results, in comparison with the MICE algorithm. Furthermore, 12 parameters affected the patient mortality prediction, which were extracted by the genetic algorithm. Accuracy of the predictive model for the patient death detection was favorable. Consequently, it is recommended to use this model in order to predict the patient mortality.