Evaluation ordinal regression model’s performance through the implementation of multilayer feed-forward neural network: a case study of hypertension
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...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Springer Nature
2024
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/111152/2/111152_Evaluation%20ordinal%20regression%20model%E2%80%99s%20performance.pdf http://irep.iium.edu.my/111152/ https://www.cureus.com/articles/229713-the-evaluation-of-ordinal-regression-models-performance-through-the-implementation-of-multilayer-feed-forward-neural-network-a-case-study-of-hypertension#!/ |
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| Summary: | 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. |
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