A Predictive Performance Analysis of Vitamin D Deficiency Severity Using Machine Learning Methods

Sambasivam, G.; Amudhavel, J.; Sathya, G.

doi:10.1109/access.2020.3002191

Cited by 15 publications

(13 citation statements)

References 40 publications

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“…The correlation with the amount of vitamin D in the blood was confirmed using saliva [33], and skin autofluorescence was confirmed to be related to vitamin D through an AEG reader in patients with type 2 diabetes [34]. The amount of vitamin D can be predicted by analysis using independent parameters such as age, sex, weight, height, body mass index (BMI), waist circumference, body fat, bone mass, exercise, sun exposure, and milk intake [35]. This study aims to develop a prediction algorithm through skin impedance measurement by suggesting a new alternative vitamin D measurement method.…”

Section: Discussionmentioning

confidence: 83%

A Novel Biosensor and Algorithm to Predict Vitamin D Status by Measuring Skin Impedance

Heo

Kim

et al. 2021

Sensors

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The deficiency and excess of vitamin D cause various diseases, necessitating continuous management; but it is not easy to accurately measure the serum vitamin D level in the body using a non-invasive method. The aim of this study is to investigate the correlation between vitamin D levels, body information obtained by an InBody scan, and blood parameters obtained during health checkups, to determine the optimum frequency of vitamin D quantification in the skin and to propose a vitamin D measurement method based on impedance. We assessed body composition, arm impedance, and blood vitamin D concentrations to determine the correlation between each element using multiple machine learning analyses and an algorithm which predicted the concentration of vitamin D in the body using the impedance value developed. Body fat percentage obtained from the InBody device and blood parameters albumin and lactate dehydrogenase correlated with vitamin D level. An impedance measurement frequency of 21.1 Hz was reflected in the blood vitamin D concentration at optimum levels, and a confidence level of about 75% for vitamin D in the body was confirmed. These data demonstrate that the concentration of vitamin D in the body can be predicted using impedance measurement values. This method can be used for predicting and monitoring vitamin D-related diseases and may be incorporated in wearable health measurement devices.

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Section: Discussionmentioning

confidence: 83%

A Novel Biosensor and Algorithm to Predict Vitamin D Status by Measuring Skin Impedance

Heo

Kim

et al. 2021

Sensors

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“…RF performed the best and achieved sensitivity (96%), negative predictive value (96%), and classification accuracy (96%). 28 Carretero et al (2021) used the data from 1002 hypertensive patients to predict vitamin D deficiency. They applied LR, SVM, RF, naive bayes, and extreme gradient boost.…”

Section: Related Studiesmentioning

confidence: 99%

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

Eşsiz,

Yüregir,

Saraç

2023

Health Informatics J

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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.

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“…Besides, they did not address the effects of the multicollinearity problem on the ML models. Only one study, the [ 34 ], attempted to predict (or classify) Vitamin D deficiency by conducting multiclass classification ML methods to classify Vitamin D status. On the other hand, in the study [ 34 ], the samples were collected from college students in the age group of 18–21.…”

Section: Introductionmentioning

confidence: 99%

“…Only one study, the [ 34 ], attempted to predict (or classify) Vitamin D deficiency by conducting multiclass classification ML methods to classify Vitamin D status. On the other hand, in the study [ 34 ], the samples were collected from college students in the age group of 18–21. The authors conducted various classification techniques but did not consider two conventional ordinal logistic and Elastic net ordinal regression models.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of the ML model selection, the literature review showed that the Ordinal logistic regression (OLR), Elastic-net ordinal regression (ENOR), Support Vector Machine (SVM), and Random Forest (RF) models are commonly applied to classification problems [ 30 , 32 – 34 , 38 – 49 ]. In Table 1 , studies using various ML models are summarized according to the metrics used, and the models compared.…”

Section: Introductionmentioning

confidence: 99%

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

Sancar,

Tabrizi

2023

BMC Med Inform Decis Mak

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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.

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A Predictive Performance Analysis of Vitamin D Deficiency Severity Using Machine Learning Methods

Cited by 15 publications

References 40 publications

A Novel Biosensor and Algorithm to Predict Vitamin D Status by Measuring Skin Impedance

A Novel Biosensor and Algorithm to Predict Vitamin D Status by Measuring Skin Impedance

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

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

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