Polynomial approach modeling among diabetic patients associated with age in rural hilly population of Dehradun district, Uttarakhand (original) (raw)

Log-linear Model of Diabetic Patients of Plateau State General Hospitals

Mathematical theory and modeling, 2017

This study investigates Diabetes Mellitus, which is a disorder that is assuming pandemic proportion worldwide. With secondary data from Plateau State General Hospitals and a log-linear model analysis carried out using statistical package (SPSS version 21), to find out if there is a relationship between diabetes, gender and Body mass index (BMI). The result shows that there is a significant relationship between female as reported to BMI and diabetes with a P - value of 0.0007, and male as reported to BMI and diabetes with a P- value of 0.000. This i­ndicates an interaction between gender, BMI and diabetes because all their P-values are less than the alpha level of 0.05. The study recommends public education on regular BMI checking, regular exercise and proper diet by both sex to avoid obesity which is generally associated with an increase in risk of diabetes mellitus. Keywords: Diabetes Mellitus, Obesity, Body mass index and log-linear analysis.

SEIITR Model for Diabetes Mellitus Distribution in Case of Insulin and Care Factors

Jurnal Inotera, 2020

This research is done to learn diabetes mellitus type SEIITR with insulin and care factors. Mathematical model type SEIITR is a mathematical model of diabetes in which the human population is divided into five groups: susceptible humans (Susceptible) S, exposed (Exposed) E, infected I without treatment, infected (Infected) IT with treatment dan recovered (Recovery) R. The SEIITR model has two fixed points, namely, a fixed point without disease and an endemic fixed point. By using basic reproduction numbers (R0), it is found that the fixed point without disease is stable if R0 < 1 and when R0 > 1. Then the fixed point without disease is unstable. The simulation shows the effect of giving insulin to changes in the value of the basic reproduction number. If the effectiveness of β decreases, the basic reproduction number decreases too. Thus, a decrease in the value of this parameter will be able to help reduce the rate of diabetes mellitus in the population.

Mathematical Regression Models for Analyzing and Forecasting Diabetes prevalence in Oman

Applied computing Journal, 2021

Diabetes mellitus has a significant impact on people's lives and drugs financial burden. On the other hand, diabetes also has substantial economic effects on countries and national health systems. Most countries spend between 5% and 20% of their total health expenditures on diabetes. This is due to the increased use of health services, lack of productivity, and the long-term demand for complications associated with diabetes, such as kidney failure, blindness, and heart problems. This is why diabetes poses a significant challenge to healthcare systems and hinders sustainable economic development. This work is concerned with proposing mathematical models characterized by accuracy and ease in predicting the number of diabetics type 2 in the Sultanate of Oman. By analyzing the proposed mathematical models of the current work (1, 2, and 3), it was found that the proposed mathematical model in Equation 6 can accurately predict the number of diabetics in Oman up to 2050. In order to te...

Some Prediction Models in the Study of Diabetic Retinopathy among known Type II Diabetes Mellitus Patients in a Southern Part of India: Various Statistical Models Approach

International Journal of Medical Sciences and Nursing Research, 2023

Background: Diabetic Mellitus is a chronic disease and metabolic disorder. DM affects about 180 million people in the presently and it is a public health problem in worldwide. To find out the risk factors and how much its influence, to identify the risk factors that influencing, to identify the presence of DR and its progression by forming mathematical equations using which was found possible with some variables and to find several stages of DR and its progression. Materials and Methods: In this study, adult population (age ≥ 18) only was taken into account for data analysis. Some structured questionnaires were used for data collection. We have done some hospital based retrospective studies among known T2DM patients. The continuous variables were expressed as mean and standard deviation and categorial variables as frequency and proportions. We have used, various prediction statistical models. Results: By multiple regression analysis, found the influencing factors in the progression of DR, predicted the probability of a T2DM patient to develop DR and found the probability of DR among diabetes up to a given period of time and using by Markov Chain Analysis found the TPM and the absorbing state in a T2DM patient and to identify as having complete vision loss. Conclusions: Statistical models were revealed that found the influenced factors and risk ratio has been computed, Number of years of DM, and progression and transition of DR which predict the chance to develop DR in a known T2DM patient.

Mathematical Modelling of Diabetes Mellitus and Associated Risk Factors in Saudi Arabia

International Journal of Simulation Systems Science & Technology

Mathematical modelling has been successfully applied to the healthcare domain and epidemiological chronic diseases, including diabetes mellitus, which is classified as an epidemic due to its high rates of global prevalence. This paper models diabetes mellitus in Saudi Arabia along with the most relevant risk factors, namely smoking, obesity, and physical inactivity for adults aged ≥25 years. The aim of this study is based on developing different mathematical models for the purpose of studying the trends in incidence rates of diabetes over 15 years (1999-2013) and to get predictions for the future level of the disease up to 2025, as this should support health policy planning and identifying the necessary costs of controlling diabetes. Different models were developed, namely Logistic Regression, Neural Networks, and Artificial Neural Networks. An overview of the performance of these models is provided to analyse their advantages and limitations. A combination of these models is performed to improve the prediction accuracy using combination methods such as AVR, WAVR, and MAJ. The combined model was validated by comparing the prediction of prevalence estimates by World Health Organization, International Diabetes Federation, and Family Health Survey from the Saudi General Authority for Statistics. Improved accuracy was achieved with this combined model in comparison to these studies.

A POLYNOMIAL LINEAR REGRESSION APPROACH TO ESTIMATE SENSITIVE PARAMETERS IN THE NOVEL DOUBLE DIABETES MODEL

International Journal of Analysis and Applications, 2020

Sensitivity analysis characterizes the changes in the model outputs due to the changes in the model parameters. In this article, we estimate the most sensitive parameters in the Novel Double Diabetes Model (NDDM) through the polynomial linear regression approach; this way we develop a direct relation between the sensitivity analysis and the paramter estimation. The NDDM has more than seventeen parameters , and estimating them simultanously is difficult. We select the most commonly used five parameters in the glucose-insulin dynamics for the sensitivity analysis. The model outputs-glucose concentrations in the plasma and the subcutaneous compartments are sensitive to the selected parameters whereas the insulin concentrations in the plasma and the subcutaneous compartment are sensitive only to the insulin transfer rate from the subcutaneous to the plasma compartment. System sensitivity of the model for the selected parameters is also in agreement with the individual sensitivities of the parameters. Consequently, we estimate the parameters which are more sensitive by the polynomial linear regression approach.

A structured additive modeling of diabetes and hypertension in Northeast India

Plos One, 2022

Background Multiple factors are associated with the risk of diabetes and hypertension. In India, they vary widely even from one district to another. Therefore, strategies for controlling diabetes and hypertension should appropriately address local risk factors and take into account the specific causes of the prevalence of diabetes and hypertension at sub-population levels and in specific settings. This paper examines the demographic and socioeconomic risk factors as well as the spatial disparity of diabetes and hypertension among adults aged 15–49 years in Northeast India. Methods The study used data from the Indian Demographic Health Survey, which was conducted across the country between 2015 and 2016. All men and women between the ages of 15 and 49 years were tested for diabetes and hypertension as part of the survey. A Bayesian geo-additive model was used to determine the risk factors of diabetes and hypertension. Results The prevalence rates of diabetes and hypertension in Northeast India were, respectively, 6.38% and 16.21%. The prevalence was higher among males, urban residents, and those who were widowed/divorced/separated. The functional relationship between household wealth index and diabetes and hypertension was found to be an inverted U-shape. As the household wealth status increased, its effect on diabetes also increased. However, interestingly, the inverse was observed in the case of hypertension, that is, as the household wealth status increased, its effect on hypertension decreased. The unstructured spatial variation in diabetes was mainly due to the unobserved risk factors present within a district that were not related to the nearby districts, while for hypertension, the structured spatial variation was due to the unobserved factors that were related to the nearby districts. Conclusion Diabetes and hypertension control measures should consider both local and non-local factors that contribute to the spatial heterogeneity. More importance should be given to efforts aimed at evaluating district-specific factors in the prevalence of diabetes within a region.

A Mathematical Model for The Epidemiology of Diabetes Mellitus with Lifestyle and Genetic Factors

Journal of Physics: Conference Series

Mathematical model to elaborate the prevalence of diabetics has been determined by diabetes complication (DC) model. In the DC model, people with diabetes were classified into two compartments, uncomplicated diabetics (D) and complicated diabetics (C). Diabetes is known as a disease caused by lifestyle and genetic factors. A bad lifestyle leads a susceptible individual to become a diabetic. Bad lifestyle is strongly influenced by risky social interaction. In the other side, a genetic factor is the main cause of the diabetes genetic disorder birth. Consider these both factors, the DC model was developed into a susceptible diabetes complication (SDC) model. Susceptible individuals were involved in the calculation of risky interactions. The SDC model is a first order nonlinear differential equation. The number and the change of individuals in each compartment can be determined from the solution of this model. In this paper, the SDC model is applied to predict changes of diabetics prevalence in the United States. As a result, the SDC model is good enough to predict the prevalence.

Exploring the Risk Factors of Diabetes in Dhaka City: Negative Binomial Regression & Logistic Regression Approach

Saudi Journal of Medical and Pharmaceutical Sciences, 2020

In the developing countries, diabetes has become one of the most indicating public health challenges. It imposes a big economic impact on the society. The Bangladeshi people hardly understand the risk factors that causes diabetes. This study mainly aims to explore the potential risk factors behind this disease through some popular statistical analysis approaches. In this regard, a field survey was conducted on the diabetes patients in Dhaka city. The analysis involves binary logistic regression, negative binomial regression, and multinomial logistic regression model according to the nature of the data. The variables family history, height, weight, gender, age, food with high cholesterol, no exercise, taking alcohol/cigarettes, hypertension, and eye problem are found significant in this study. In addition, the count data related to the number of time(s) a patient checks blood glucose level is also analyzed and interpreted. From the overall findings, the study suggests to become extra...

Predictive data modeling of human type II diabetes related statistics

Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 2009

During the course of routine Type II treatment of one of the authors, it was decided to derive predictive analytical Data Models of the daily sampled vital statistics: namely weight, blood pressure, and blood sugar, to determine if the covariance among the observed variables could yield a descriptive equation based model, or better still, a predictive analytical model that could forecast the expected future trend of the variables and possibly eliminate the number of finger stickings required to montior blood sugar levels. The personal history and analysis with resulting models are presented.