Robust Method in Multiple Linear Regression Model on Diabetes Patients

Rusiman, Mohd Saifullah; Nor, Siti Nasuha Md; Suparman, _; Razali, Siti Noor Asyikin Mohd

doi:10.13189/ms.2020.081306

Cited by 7 publications

(8 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Diabetes is a prevalent [21,22] and incurable disease. It is a protracted health condition that affects the human body's ability to produce energy from food.…”

Section: Discussionmentioning

confidence: 99%

Female Diabetic Prediction in India Using Different Learning Algorithms

Bose¹,

Bandyopadhyay²,

Bhaumik³

et al. 2021

ujph

View full text Add to dashboard Cite

“…Diabetes is a prevalent [21,22] and incurable disease. It is a protracted health condition that affects the human body's ability to produce energy from food.…”

Section: Discussionmentioning

confidence: 99%

Female Diabetic Prediction in India Using Different Learning Algorithms

Bose¹,

Bandyopadhyay²,

Bhaumik³

et al. 2021

ujph

View full text Add to dashboard Cite

“…MLR is an extension of simple linear regression [19] and is usually applied for many statistical analyses and is also known as a method that can evaluate the association among the dependent and independent variables [20]. MLR model has two key assumptions which are normality distribution and multicollinearity among explanatory variables.…”

Section: Research Methods 31 Multiple Linear Regression (Mlr)mentioning

confidence: 99%

An adjustment degree of fitting on fuzzy linear regression model toward manufacturing income

Ramly

Rusiman

Shafi

et al. 2023

IJ-AI

View full text Add to dashboard Cite

Regression analysis is a popular tool used in data analysis, whereas fuzzy regression is usually used for analyzing uncertain and imprecise data. In the industrial area, the company usually has problems in predicting the future manufacturing income. Therefore, a new approach model is needed to solve the future company prediction income. This article analyzed the manufacturing income by using the multiple linear regression (MLR) model and fuzzy linear regression (FLR) model proposed by Tanaka and Zolfaghari, involving 9 explanatory variables. In order to find the optimum of the FLR model, the degree of fitting (H) was adjusted between 0 to 1. The performance of three methods has been measured by using mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The analysis proved that FLR with Zolfaghari’s model with the degree of fitting of 0.025 outperformed the MLR and FLR with Tanaka’s model with the smallest error value. In conclusion, the manufacturing income is directly proportional to 6 independent variables. Furthermore, the manufacturing income is inversely proportional to 3 independent variables. This model is suitable in predicting future manufacturing income.

show abstract

“…MPM outlier values were identified using a robust mean absolute deviation method (MAD) [ 33 ], and 41 participants (7% of the selected sample) with MPM values of more than 3MAD were excluded from the analysis, leaving a total of 560 participants ( Figure S1 ). The MPM were divided into 14 groups according to their chemical structure, and each group included all the aglycone, glucuronide, and sulfate forms.…”

Section: Methodsmentioning

confidence: 99%

Microbial Phenolic Metabolites in Urine Are Inversely Linked to Certain Features of Metabolic Syndrome in Spanish Adolescents

et al. 2022

View full text Add to dashboard Cite

(1) Background: To explore the association between microbial phenolic metabolites (MPM) and metabolic syndrome (MetS) and its clinical features in adolescents aged 12.02 ± 0.41 years. (2) Methods: a cross-sectional study was conducted in 560 participants at baseline in the SI! Program for Secondary Schools trial. The following MPM, coumaric acids (m-, o-, p- coumaric acids), dihydroxyphenylpropionic acid, dihydroresveratrol, enterolignans, gallic acid, hydroxybenzoic acids, hydroxyphenylacetic acid, hydroxytyrosol, protocatechuic acid, syringic acid, urolithins (A, B), and vanillic acid, were analyzed by HPLC-LTQ-Orbitrap-HRMS. MetS and its clinical features were defined in accordance with the International Diabetes Federation. (3) Results: Out of all MPM, urolithin A was inversely associated with the diastolic blood pressure z-score. Urolithin B was inversely associated with the MetS score and waist circumference z-score. Additionally, higher levels of gallic acid were associated with lower odds of presenting MetS (OR = 0.85, 95% CI: 0.77; 0.93) and abdominal obesity (OR = 0.93, 95% CI: (0.89; 0.98). Higher urolithin B levels were inversely associated with abdominal obesity (OR = 0.94, 95% CI: 0.89; 0.98) and high blood glucose (OR = 0.92, 95% CI:0.88; 0.96); (4) Conclusions: gallic acid, urolithin A and B were inversely associated with lower odds of presenting MetS or some of its clinical features in adolescents. This is the first study that evaluates several MPM with MetS in adolescents, highlighting the importance of MPM on cardiometabolic health at early life stages.

show abstract

Robust Method in Multiple Linear Regression Model on Diabetes Patients

Cited by 7 publications

References 4 publications

Female Diabetic Prediction in India Using Different Learning Algorithms

Female Diabetic Prediction in India Using Different Learning Algorithms

An adjustment degree of fitting on fuzzy linear regression model toward manufacturing income

Microbial Phenolic Metabolites in Urine Are Inversely Linked to Certain Features of Metabolic Syndrome in Spanish Adolescents

Contact Info

Product

Resources

About