Machine learning approach for the detection of vitamin D level: a comparative study.

BACKGROUND: After the World Health Organization declared the COVID-19 pandemic, the role of Vitamin D has become even more critical for people worldwide. The most accurate way to define vitamin D level is 25-hydroxy vitamin D(25-OH-D) blood test. However, this blood test is not always feasible. Most data sets used in health science research usually contain highly correlated features, which is referred to as multicollinearity problem. This problem can lead to misleading results and overfitting problems in the ML training process. Therefore, the proposed study aims to determine a clinically acceptable ML model for the detection of the vitamin D status of the North Cyprus adult participants accurately, without the need to determine 25-OH-D level, taking into account the multicollinearity problem. METHOD: The study was conducted with 481 observations who applied voluntarily to Internal Medicine Department at NEU Hospital. The classification performance of four conventional supervised ML models, namely, Ordinal logistic regression(OLR), Elastic-net ordinal regression(ENOR), Support Vector Machine(SVM), and Random Forest (RF) was compared. The comparative analysis is performed regarding the model's sensitivity to the participant's metabolic syndrome(MtS)'positive status, hyper-parameter tuning, sensitivities to the size of training data, and the classification performance of the models. RESULTS: Due to the presence of multicollinearity, the findings showed that the performance of the SVM(RBF) is obviously negatively affected when the test is examined. Moreover, it can be obviously detected that RF is more robust than other models when the variations in the size of training data are examined. This experiment's result showed that the selected RF and ENOR showed better performances than the other two models when the size of training samples was reduced. Since the multicollinearity is more severe in the small samples, it can be concluded that RF and ENOR are not affected by the presence of the multicollinearity problem. The comparative analysis revealed that the RF classifier performed better and was more robust than the other proposed models in terms of accuracy (0.94), specificity (0.96), sensitivity or recall (0.94), precision (0.95), F1-score (0.95), and Cohen's kappa (0.90). CONCLUSION: It is evident that the RF achieved better than the SVM(RBF), ENOR, and OLR. These comparison findings will be applied to develop a Vitamin D level intelligent detection system for being used in routine clinical, biochemical tests, and lifestyle characteristics of individuals to decrease the cost and time of vitamin D level detection.

Development and Validation of a Near Miss Scale for Assessing Gambling Tendency.

Although near-miss is an important tendency indicator for gambling addiction, no scale has been developed to evaluate these feelings. In this study, the aim is to develop a Near Miss Scale (NMS) to assess the tendency of gambling. In the first step, a 38-item measurement tool was prepared by the first author, which was examined by 8 experts. According to their comments and opinions, a 32-item 5-point Likert-type pre-form was created. The study was conducted with 600 gamblers in Northern Cyprus between December 2018-March 2019 and data from 563 of them were included in the statistical analysis. In the questionnaire, Socio-demographic form, Gambling Craving Scale (GCS), South Oaks Gambling Screening Test (SOGST) and NMS were used. With the SPSS 23 and R Studio statistical programs, after calculating the item-total correlations of the items in the NMS form, items with low item total-correlation values were excluded from the scale and 30 items were analysed statistically. In the study, it was seen that the factor loads of the relevant items in NMS were between .715 and .896. Confirmatory factor analysis (CFA) showed that a single factor model in the scale was valid. NMS had a positive correlation with SOGST (r = 0.601) and GCS (r = 0.752). The 2-week test-retest results of NMS with a Cronbach alpha of 0.981 were determined as 0.972. The validity and reliability results suggest that NMS is a valid and reliable as 30-item, one-dimensional measurement tool for assessing gambling tendency among gamblers.

A new alternative estimation method for Liu-type logistic estimator via particle swarm optimization: an application to data of collapse of Turkish commercial banks during the Asian financial crisis.

In the existence of multicollinearity problem in the logistic model, some important problems may occur in the analysis of the model, such as unstable maximum likelihood estimator with very high standard errors, false inferences. The Liu-type logistic estimator was proposed as two-parameter estimator to overcome multicollinearity problem in the logistic model. In the existing previous studies, the (k, d) pair in this shrinkage estimator is estimated by two-phase methods. However, since the different estimators can be utilized in the estimation of d, optimal choice of the (k, d) pair provided using the two-phase approaches is not guaranteed to overcome multicollinearity. In this article, a new alternative method based on particle swarm optimization is suggested to estimate (k, d) pair in Liu-type logistic estimator, simultaneously. For this purpose, an objective function that eliminates the multicollinearity problem, provides minimization of the bias of the model and improvement of the model's predictive performance, is developed. Monte Carlo simulation study is conducted to show the performance of the proposed method by comparing it with existing methods. The performance of the proposed method is also demonstrated by the real dataset which is related to the collapse of commercial banks in Turkey during Asian financial crisis.