Evaluation ordinal regression model’s performance through the implementation of multilayer feed-forward neural network: a case study of hypertension - Adnan, Mohamad Nasarudin; Wan Ahmad, Wan Muhamad Amir; Shahzad, Hazik; Awais, Faiza; Aleng, Nor Azlida; Mohd Noor, Nor Farid; Mohd Ibrahim, Mohamad Shafiq; Mohd Noor, Noor Maizura (Springer Nature, 2024)
Background: Hypertension, or high blood pressure, is a common medical condition that affects a significant portion of the global population. It is a major risk factor for cardiovascular diseases, stroke, and kidney disorders. Objective: The objective of this study is to create and validate a model that combines ordered logistic regression and multilayer feed-forward neural networks to identify and analyze the factors associated with hypertension patients who also have dyslipidemia. Material and Methods: A total of 33 participants were enrolled from the Hospital Universiti Sains Malaysia (USM) for this study. In this study, advanced computational statistical modeling techniques were utilized to examine the relationship between hypertension status and several potential predictors. The R-Studio software and syntax were implemented to establish the relationship between hypertension status with the predictors. Results: The statistical analysis showed that the developed methodology demonstrates the good of model fitting through the value of predicted mean square error, mean absolute deviance, and accuracy. To evaluate model fitting, the data in this study was divided into distinct training and testing datasets. The findings revealed that the results strongly support the superior predictive capability of the hybrid model technique. In this case, five variables are considered: marital status, smoking status, systolic blood pressure, fasting blood sugar levels, and high-density lipoprotein levels. It’s important to note that all of them affect the hazard ratio: marital (β1 : -17.12343343; p < 0.25), smoke (β2 : 1.86069121; p < 0.25), systolic (β3 : 0.05037332; p < 0.25), fasting blood sugar (β4 : -0.53880322; p < 0.25) and high-density lipoprotein (β5 : 5.38065556; p < 0.25). Conclusion: This research aims to develop and extensively evaluate a hybrid approach that combines bootstrapping, multilayer feedforward neural network, and ordered logistic regression. The statistical methods employed in this study using R show that regression modeling surpasses R-squared values in predicting mean squared error. The study's conclusion provides strong evidence for the superiority of the hybrid model technique.