Logit model in prospective coronary heart disease (CHD) risk factors prediction in Saudi population

Abstract

Analysis through logistic regression explored to investigate the relationship between binary or multivariable ordinal response probability and in one or more explanatory variables. The main objectives of this study to investigate advanced prediction risk factor of Coronary Heart Disease (CHD) using a logit model. Attempts made to reduce risk factors, increase public or professional awareness. Logit model used to evaluate the probability of a person develop CHD, considering any factors such as age, gender, high low-density lipoprotein (LDL) cholesterol, low high-density lipoprotein (HDL) cholesterol, high blood pressure, family history of CHD younger than 45, diabetes, smoking, being post-menopausal for women and being older than 45 for men. Logit concept of brief statistics described with slight modification to estimate the parameters testing for the significance of the coefficients, confidence interval fits the simple, multiple logit models. Besides, interpretation of the fitted logit regression model introduced. Variables showing best results within the scientific context, good explanation data assessed to fit an estimated logit model containing chosen variables, this present experiment used the statistical inference procedure; chi-square distribution, likelihood ratio, Score, or Wald test and goodness-of-fit. Health promotion started with increased public or professional awareness improved for early detection of CHD, to reduce the risk of mortality, aimed to be Saudi vision by 2030.

Investigation of Demographic Characteristics of Essential Hypertension Patients Admitted to Cumhuriyet University Health Services Practice and Research Hospital

“…The prevalence of HT among the participants in the study was found to be much higher than the world average. However, the results are closer to the prevalence found in the country where the study was conducted (15)(16)(17)(18)(19).…”

Construction of a Nomogram Model to Identify Atherosclerotic Cardiovascular Disease in Patients with Type 2 Diabetes Mellitus

“…Second, the extensive array of clinical tests and examination protocols for this population overcomes the limitations associated with incomplete data in some physical examinations of the general population. Various models for com-mon diseases include logistic regression models, classification tree models, backpropagation (BP) neural networks, and nomograms [15][16][17][18]. In this study, we opted to create a nomogram for identifying ASCVD in T2DM patients due to its advantages in risk factor screening, consideration of interaction terms, and risk assessment [19].…”

Logistic Regression Model based on heart disease and Its Potential Influencing Factors

“…The research chooses factors such as age, gender, the measure of cholesterol, blood pressure, family history, smoking etc. and use some basic methods in logistic regression model to find out the relationship between the variables [1]. Thirugnanam et.al conducted research to find out the most influential factors as well as build an accurate prediction model using methods such as support vector machine, decision tree and logistic regression [2].…”