Prediction of Factors for Patients with Hypertension and Dyslipidemia Using Multilayer Feedforward Neural Networks and Ordered Logistic Regression Analysis: A Robust Hybrid Methodology.
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...
| Publicado en: | Makara Journal of Health Research Vol. 27; no. 2; pp. 135 - 143 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Universitas Indonesia
Aug2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173676791&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173676791 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23563664 KTGB jtl: Makara Journal of Health Research issn: 23563664 maglogo: N pubinfo: dt: Aug2023 vid: 27 iid: 2 pid: 27987 pub: Universitas Indonesia artinfo: ui: 173676791 173676791 173676791 10.7454/msk.v27i2.1458 173676791 ppf: 135 ppct: 8 formats: tig: atl: Prediction of Factors for Patients with Hypertension and Dyslipidemia Using Multilayer Feedforward Neural Networks and Ordered Logistic Regression Analysis: A Robust Hybrid Methodology. aug: au: Ahmad, Wan Muhamad Amir W. Bin Adnan, Mohamad Nasarudin Yusop, Norhayati Bin Shahzad, Hazik Ghazali, Farah Muna Mohamad Aleng, Nor Azlida Noor, Nor Farid Mohd affil: School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, Malaysia sug: subj: Hypertension Risk Factors Hyperlipidemia Neural Networks (Computer) Theory Construction Risk Assessment Models, Statistical Evaluation Malaysia Human Multiple Logistic Regression Data Analysis Software ab: 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 (ß1: -0.00664; p < 0.25), diabetes status (ß2: 0.62332; p < 0.25), diastolic reading (ß3: 0.08160; p < 0.25), height measurement (ß4: -0.05411; p < 0.25), coronary heart disease incidence (ß5: 1.42544; p < 0.25), triglyceride reading (ß6: 0.00616; p < 0.25), and waist reading (ß7: -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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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