Prediction of Factors for Patients with Hypertension and Dyslipidemia Using Multilayer Feedforward Neural Networks and Ordered Logistic Regression Analysis: A Robust Hybrid Methodology

Authors

Wan Muhamad Amir W Ahmad, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, MalaysiaFollow
Mohamad Nasarudin Bin Adnan, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, Malaysia
Norhayati Yusop, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, Malaysia
Hazik Bin Shahzad, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, MalaysiaFollow
Farah Muna Mohamad Ghazali, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, Malaysia
Nor Azlida Aleng, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Malaysia
Nor Farid Mohd Noor, Faculty of Medicine, Universiti Sultan Zainal Abidin, Medical Campus, Kuala Terengganu 20400, Malaysia

ORCID ID

Wan Muhamad Amir W Ahmad : 0000-0003-2366-3918

Mohamad Nasarudin Bin Adnan : 0009-0008-4759-4869

Norhayati Yusop : 0000-0002-2867-1290

Hazik Bin Shahzad : 0000-0002-6524-7261

Farah Muna Mohamad Ghazali : 0000-0001-6772-6779

Nor Azlida Aleng : 0000-0003-1111-3388

Nor Farid Mohd Noor : 0000-0001-6432-781X

Abstract

Background: Hypertension is characterized by abnormally high arterial blood pressure and is a public health problem with a high prevalence of 20%–30% worldwide. This research combined multiple logistic regression (MLR) and multilayer feedforward neural networks to construct and validate a model for evaluating the factors linked with hypertension in patients with dyslipidemia.

Methods: A total of 1000 data entries from Hospital Universiti Sains Malaysia and advanced computational statistical modeling methodologies were used to evaluate seven traits associated with hypertension. R-Studio software was utilized. Each sample's statistics were calculated using a hybrid model that included bootstrapping.

Results: Variable validation was performed by using the well-established bootstrap-integrated MLR technique. All variables affected the hazard ratio as follows: total cholesterol (β₁: −0.00664; p < 0.25), diabetes status (β₂: 0.62332; p < 0.25), diastolic reading (β₃: 0.08160; p < 0.25), height measurement (β₄: −0.05411; p < 0.25), coronary heart disease incidence (β₅: 1.42544; p < 0.25), triglyceride reading (β₆: 0.00616; p < 0.25), and waist reading (β₇: −0.00158; p < 0.25).

Conclusions: A hybrid approach was developed and extensively tested. The hybrid technique is superior to other standalone techniques and allows an improved understanding of the influence of variables on outcomes.

References

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Recommended Citation

Ahmad WM, Adnan MN, Yusop N, Shahzad HB, Ghazali FM, Aleng NA, et al. Prediction of Factors for Patients with Hypertension and Dyslipidemia Using Multilayer Feedforward Neural Networks and Ordered Logistic Regression Analysis: A Robust Hybrid Methodology. Makara J Health Res. 2023;27.

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