An assessment of the risk factors for vitamin D deficiency using a decision tree model

Improved linear growth after routine zinc supplementation in preterm very low birth weight infants

BACKGROUND

This study was designed (1) to compare growth, morbidity and mortality in < 33-week gestational age (GA) (very preterm, VPT) or very low birth weight (BW < 1500 grams, VLBW) infants before (Epoch-1) and after implementing routine enteral zinc (Zn) supplementation (Epoch-2) to meet recommendations, and (2) to assess serum Zn levels and associated variables.

METHODS

Single-center prospective cohort of 826 infants. The primary outcome was the change (Δ) in Z-scores of accurate length (Δlengthz), weight and head circumference from birth to discharge home.

RESULTS

In Epoch-2 vs Epoch-1 Δlengthz adjusted for confounding variables increased by 0.27 [95% confidence interval (CI) 0.13, 0.42, P < 0.001]. However, morbidity and mortality did not change. In Epoch-2 Zn decreased with GA and postnatal age: low ( < 0.74 mcg/mL) levels were observed in 51% infants. Retinopathy of prematurity (ROP) was independently associated with the lowest Zn [adjusted odds ratio 0.042 (CI 0.006, 0.306), area under the curve=0.928].

CONCLUSION

Routine enteral Zn supplementation was independently associated with improved linear growth but did not prevent occurrence of low Zn. ROP was independently associated with the lowest Zn.

IMPLICATIONS

Multicenter studies are needed to assess whether dosage of enteral Zn should be increased and whether Zn could help prevent ROP.

IMPACT

Implementation of routine enteral zinc (Zn) supplementation was associated with improved linear growth from birth to discharge and a more frequent physiologic growth curve in preterm very low birth weight infants. Serum Zn levels decreased with gestational age and with postnatal age. Low serum Zn levels were observed frequently despite routine Zn supplementation as currently recommended, which suggests a need to re-evaluate current enteral zinc supplementation guidelines for this population. Retinopathy of prematurity among infants < 33 weeks' gestation was independently associated with low gestational age, low birthweight, stage of bronchopulmonary dysplasia and the lowest serum Zn level.

Assessing the relative importance of vitamin D deficiency in cardiovascular health

Previous research has suggested a potential link between vitamin D (VD) deficiency and adverse cardiovascular health outcomes, although the findings have been inconsistent. This study investigates the association between VD deficiency and cardiovascular disease (CVD) within the context of established CVD risk factors. We utilized a Random Forest model to predict both CVD and VD deficiency risks, using a dataset of 1,078 observations from a rural Chinese population. Feature importance was evaluated using SHapley Additive exPlanations (SHAP) to discern the impact of various risk factors on the model's output. The results showed that the model for CVD prediction achieved a high accuracy of 87%, demonstrating robust performance across precision, recall, and F1 score metrics. Conversely, the VD deficiency prediction model exhibited suboptimal performance, with an accuracy of 52% and lower precision, recall, and F1 scores. Feature importance analysis indicated that traditional risk factors such as systolic blood pressure, diastolic blood pressure, age, body mass index, and waist-to-hip ratio significantly influenced CVD risk, collectively contributing to 70% of the model's predictive power. Although VD deficiency was associated with an increased risk of CVD, its importance in predicting CVD risk was notably low. Similarly, for VD deficiency prediction, CVD risk factors such as systolic blood pressure, glucose levels, diastolic blood pressure, and body mass index emerged as influential features. However, the overall predictive performance of the VD deficiency prediction model was weak (52%), indicating the absence of VD deficiency-related risk factors. Ablation experiments confirmed the relatively lower importance of VD deficiency in predicting CVD risk. Furthermore, the SHAP partial dependence plot revealed a nonlinear relationship between VD levels and CVD risk. In conclusion, while VD deficiency appears directly or indirectly associated with increased CVD risk, its relative importance within predictive models is considerably lower when compared to other risk factors. These findings suggest that VD deficiency may not warrant primary focus in CVD risk assessment and prevention strategies, however, further research is needed to explore the causal relationship between VD deficiency and CVD risk.

Applying data mining techniques to predict vitamin D deficiency in diabetic patients

Vitamin D is among the vitamins necessary for both adults' and children's health. It plays a significant role in calcium absorption, the immune system, cell proliferation and differentiation, bone protection, skeletal health, rickets, muscle health, heart health, disease pathogenesis and severity, glucose metabolism, glucose intolerance, varying insulin secretion, and diabetes. Because the 25-hydroxyvitamin D (25OHD) test, which is used to measure vitamin D is expensive and may not be covered in healthcare benefits in many countries, this study aims to predict vitamin D deficiency in diabetic patients. The prediction method is based on data mining techniques combined with feature selection by using historical electronic health records. The results were compared with a filter-based feature selection algorithm, namely relief-F. Non-valuable features were eliminated effectively with the relief-F feature selection method without any performance loss in classification. The performances of the methods were evaluated using classification accuracy (ACC), sensitivity, specificity, F1-score, precision, kappa results, and receiver operating characteristic (ROC) curves. The analyses have been conducted on a vitamin D dataset of diabetic patients and the results show that the highest classification accuracy of 97.044% was obtained for the support vector machines (SVM) model using radial kernel that contains 18 features.

Zinc and its role in vitamin D function

Zinc is an essential mineral with an important relationship with vitamin D. Studies have found that reduced blood zinc levels could predict vitamin D deficiency in adolescent girls, while zinc supplementation increased vitamin D levels in postmenopausal women. In vitro studies using human peritoneal macrophages have found that zinc induced the release of calcitriol (1,25-dihydroxycholecalciferol). Zinc also acts as a cofactor for vitamin D functions, as the transcriptional activity of vitamin D-dependent genes relies on zinc to exert pleiotropic functions, including mineral ion regulation. Vitamin D could also induce zinc transporters to regulate zinc homeostasis. Together, zinc and vitamin D in adequate concentrations help maintain a healthy musculoskeletal system and beyond; however, deficiency in either of these nutrients can result in various disorders affecting almost all body systems. This brief article will focus on the role of zinc in vitamin D functions.

Online Learning Behavior Feature Mining Method Based on Decision Tree

This research mainly discusses the design of online learning behavior feature mining method based on decision tree. Data collection is the real-time collection of online learning behavior data from distance learning websites. OWC (Office Web Component) technology is used to draw real-time charts on the page. Online learning students are selected as the research object, and the student's system log data and questionnaire data are selected. When combining the pre-pruning method and the post-pruning method to make decisions after the tree is pruned, the same source data is used to adjust, test and evaluate the decision tree model. The evaluation process to generate a complete decision tree is completed by the c4.5tree algorithm in C4.5, which can be named with a suffix of .names The type definition file is used to record the type of each attribute item or the range of possible values. In the study, the prediction accuracy rate of predicting learning effect based on "online learning behavior" reached more than 66%.

Higher Genetically Predicted Triglycerides, LDL, and HDL Increase the Vitamin D Deficiency: A Mendelian Randomization Study

Introduction

It has been proven that high body mass index (BMI) levels can cause vitamin D deficiency, but the mechanism is still unclear. Therefore, this study attempts to explain this phenomenon from the perspective of blood lipid by using mendelian randomization (MR).

Methods

Genome-wide association studies (GWAS) summary datasets for serum lipids were obtained from the Global Lipids Genetics Consortium (GLGC). Vitamin D deficiency outcome data were acquired from the UK Biobank samples. Single-variable MR (SVMR) and multi-variable MR (MVMR) analyses were conducted using the TwoSampleMR package based on R 4.0.3. The four main methods were the random-effect inverse-variance weighted (IVW), MR-Egger, weighted-median method, and weighted mode.

Results

In the SVMR of serum lipid/apolipoprotein levels on serum vitamin D level, it was found that elevated serum triacylglycerol (IVW, OR = 0.85, 95%CI:0.81-0.89, P < 0.001), low-density lipoprotein (LDL) (IVW, OR = 0.93, 95%CI:0.90-0.95, P < 0.001), and high-density lipoprotein (HDL) (IVW, OR = 0.95, 95%CI:0.91-0.98, P < 0.001) levels all had a causal relationship with vitamin D deficiency, but significant pleiotropy was detected in the triacylglycerol (P = 0.001) and HDL (P = 0.003) analysis. MVMR analysis results were consistent with SVMR.

Conclusion

By using single-variable mendelian randomization and multi-variable mendelian randomization methods, we identified that the elevated serum triacylglycerol, LDL, and HDL levels all had a causal relationship with vitamin D deficiency. Taking into account the significant pleiotropy demonstrated in this study, the conclusions of this study should be treated with caution.

Potential value and impact of data mining and machine learning in clinical diagnostics

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.

Development of the decision tree model for distinguishing individuals of Chinese four surnames from Zhanjiang Han population based on Y-STR haplotypes

Co-separation studies between surnames and Y chromosome genetic markers are beneficial to revealing population migrations, surname origins, population formation histories and forensic familial searching. Genetic distributions of 27 Y-STRs in Chinese four surnames (Li, Lin, Chen and Huang) from Zhanjiang Han population were investigated. Meanwhile, we tried to develop a decision tree model for surname predictions based on Y-STR haplotypes. Allelic frequencies of 27 Y-STRs showed that unique alleles were only observed in a certain surname; besides, some alleles displayed higher frequencies in a certain surname than those in other surnames, implying these alleles might be employed as the useful indicators for surname predictions. Haplotype match probability values of 27 Y-STRs in these surnames revealed that the system could be used as a valuable tool for forensic male identification. The developed decision tree model performed well for the training set with the accuracy of 0.9860 and obtained the relatively high accuracy (>0.70) for surname predictions of the testing set. To sum up, we explored the power of the machine learning to the surname predictions based on obtained Y-STR haplotypes, which showed promising application values in forensic familial searching.

Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics

The purpose of this research was to create a Matching Consignees/Shippers Recommendation System (MCSRS). We used the association rule to identify product associations, the clustering technique to group shippers and consignees according to behaviors when receiving goods from similar shipper groups, and the decision tree to identify possible matches between shippers and consignees. Finally, Monte Carlo simulation was used to estimate potential revenue. The case study is a courier company in Thailand. The results showed that garment products and clothes were the products with the highest association. Shippers and consignees of these products were segmented according to recency, frequency, monetary factors, number of customers, number of product items, weight, and day. Three rules are proposed that enabled the assignment of 8 consignees to 56 shippers with an estimated increase in revenue by 36%. This approach helps decision-makers to develop an effective cost-saving new marketing, inclusive strategy quickly.