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1860798337751449600
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INTELEK Repository
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| collection |
Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3
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| copyright |
Copyright©PWB2025
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Malaysia
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| date |
2024-09-08
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General Document
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17333
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UniSZA
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17333_d0c2367714c25f7.pdf
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| person |
Nurul Alia Azizan
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oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=17333
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Server storage
Scanned document
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17333 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=17333 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 General Document Malaysia Library Staff (Top Management) Library Staff (Management) Library Staff (Support) Terengganu Faculty of Bio-resources & Food Industry English application/pdf 1.5 Server storage Scanned document Universiti Sultan Zainal Abidin UniSZA Private Access Universiti Sultan Zainal Abidin Copyright©PWB2025 Peninsular Malaysia Adobe PDF Library 11.0 Heavy Metals Principal Component Analysis (PCA) Public Health Risk Prediction Model 2024-09-08 Nurul Alia Azizan Mild Cognitive Impairment (MCI) Air pollution Environmental exposure Elderly population Artificial Neural Network (ANN) Environmetric analysis Agglomerative Hierarchical Clustering (AHC) Cognitive function Air quality monitoring PM2.5 2024_A Prediction Model Of Risk Factors Of Mild Cognitive Impairment (MCI) Among Elderly In Peninsular Malaysia Introduction: Urbanization and anthropogenic activities contribute to air pollution, which has been linked to neurological disorders and cognitive impairment in older adults. However, most studies focus on the prevalence of cognitive impairment and its association with metabolic factors rather than air pollutants. The objectives of this study were to characterize the air quality monitoring stations in Peninsular Malaysia, to identify the most significant air pollutant agents, to determine the best input parameters of Mild Cognitive Impairment (MCI) in the selected study area, and to develop a prediction model of MCI among the elderly. Methodology: In Phase 1, environmetric techniques such as principal component analysis (PCA) and agglomerative hierarchical clustering (AHC) analysis were used in clustering the air monitoring stations, and sensitivity analysis (SA) and artificial neural network (ANN) were used to identify the significant air pollutants. In Phase 2, a cross-sectional comparative study among 137 community-dwelling elderly people aged 60 years and above, living in the High Polluted Region (HPR) and Low Polluted Region (LPR), were recruited. Sociodemography, anthropometry, health status, lifestyle practices, and cognitive function data were collected using a questionnaire and the Mini-Mental State Examination (MMSE) tools. Hair samples were collected to be analysed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for heavy metal determination. After preprocessing the descriptive data and tabulating the cognitive score of less than 24 with other variables, environmetric techniques were used to determine the best input parameters of MCI. Input parameters of MCI were applied with ANN to develop the prediction model in Phase 3. Results: Phase 1 showed 36 of 47 stations required additional analysis using AHC, according to the PCA factor scores. LPR (seven stations), MPR (20 stations), and HPR (nine stations) were created from AHC and shared the same characteristics. For validation, the clusters discriminated well with an average of 83.89% correct classification. PM2.5 showed the most significant air pollutants compared to other pollutant agents. Three clusters of HPR (27.289 µg/m³), MPR (20.427 µg/m³), and LPR (17.897 µg/m³) were produced based on PM2.5. Phase 2 found a greater proportion of participants with MCI in HPR than in LPR areas (37.5% vs 32.1%). The mean concentrations of heavy metals in HPR were found to be in the following order (mg/kg): Zn > Mg > Al > Cr > Cu > Mn > Pb > As > Se > Co > Cd. All elements except Ni were higher than LPR. PM2.5, PM10, NO2, CO, Cu, Pb, Mn, BMI, living area, years of education, source of income, and hypercholesterolemia were recommended as the best input parameters. The prediction model with a high R² value (0.83) showed ANN was an effective tool for computing the risk factors of MCI in Phase 3. Conclusion: The utilization of statistical approaches and MCI prediction modeling showed a positive impact on Sustainable Development Goals (SDGs) by promoting health and well-being, ensuring quality education, and supporting decent work and economic growth. Its future application has the potential to enhance preventive strategies, personalized interventions, and public health policies, leading to improved cognitive health outcomes for individuals and communities, aligning with the overall sustainable development agenda. uuid:3bac5d9e-eace-4fb4-bf09-b7b4a0b9f680 17333_d0c2367714c25f7.pdf 281 317 Thesis
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| spellingShingle |
2024_A Prediction Model Of Risk Factors Of Mild Cognitive Impairment (MCI) Among Elderly In Peninsular Malaysia
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| state |
Terengganu
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| subject |
317
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| summary |
Introduction: Urbanization and anthropogenic activities contribute to air pollution, which has been linked to neurological disorders and cognitive impairment in older adults. However, most studies focus on the prevalence of cognitive impairment and its association with metabolic factors rather than air pollutants. The objectives of this study were to characterize the air quality monitoring stations in Peninsular Malaysia, to identify the most significant air pollutant agents, to determine the best input parameters of Mild Cognitive Impairment (MCI) in the selected study area, and to develop a prediction model of MCI among the elderly. Methodology: In Phase 1, environmetric techniques such as principal component analysis (PCA) and agglomerative hierarchical clustering (AHC) analysis were used in clustering the air monitoring stations, and sensitivity analysis (SA) and artificial neural network (ANN) were used to identify the significant air pollutants. In Phase 2, a cross-sectional comparative study among 137 community-dwelling elderly people aged 60 years and above, living in the High Polluted Region (HPR) and Low Polluted Region (LPR), were recruited. Sociodemography, anthropometry, health status, lifestyle practices, and cognitive function data were collected using a questionnaire and the Mini-Mental State Examination (MMSE) tools. Hair samples were collected to be analysed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for heavy metal determination. After preprocessing the descriptive data and tabulating the cognitive score of less than 24 with other variables, environmetric techniques were used to determine the best input parameters of MCI. Input parameters of MCI were applied with ANN to develop the prediction model in Phase 3. Results: Phase 1 showed 36 of 47 stations required additional analysis using AHC, according to the PCA factor scores. LPR (seven stations), MPR (20 stations), and HPR (nine stations) were created from AHC and shared the same characteristics. For validation, the clusters discriminated well with an average of 83.89% correct classification. PM2.5 showed the most significant air pollutants compared to other pollutant agents. Three clusters of HPR (27.289 µg/m³), MPR (20.427 µg/m³), and LPR (17.897 µg/m³) were produced based on PM2.5. Phase 2 found a greater proportion of participants with MCI in HPR than in LPR areas (37.5% vs 32.1%). The mean concentrations of heavy metals in HPR were found to be in the following order (mg/kg): Zn > Mg > Al > Cr > Cu > Mn > Pb > As > Se > Co > Cd. All elements except Ni were higher than LPR. PM2.5, PM10, NO2, CO, Cu, Pb, Mn, BMI, living area, years of education, source of income, and hypercholesterolemia were recommended as the best input parameters. The prediction model with a high R² value (0.83) showed ANN was an effective tool for computing the risk factors of MCI in Phase 3. Conclusion: The utilization of statistical approaches and MCI prediction modeling showed a positive impact on Sustainable Development Goals (SDGs) by promoting health and well-being, ensuring quality education, and supporting decent work and economic growth. Its future application has the potential to enhance preventive strategies, personalized interventions, and public health policies, leading to improved cognitive health outcomes for individuals and communities, aligning with the overall sustainable development agenda.
|
| title |
2024_A Prediction Model Of Risk Factors Of Mild Cognitive Impairment (MCI) Among Elderly In Peninsular Malaysia
|
| title_full |
2024_A Prediction Model Of Risk Factors Of Mild Cognitive Impairment (MCI) Among Elderly In Peninsular Malaysia
|
| title_fullStr |
2024_A Prediction Model Of Risk Factors Of Mild Cognitive Impairment (MCI) Among Elderly In Peninsular Malaysia
|
| title_full_unstemmed |
2024_A Prediction Model Of Risk Factors Of Mild Cognitive Impairment (MCI) Among Elderly In Peninsular Malaysia
|
| title_short |
2024_A Prediction Model Of Risk Factors Of Mild Cognitive Impairment (MCI) Among Elderly In Peninsular Malaysia
|
| title_sort |
2024_a prediction model of risk factors of mild cognitive impairment (mci) among elderly in peninsular malaysia
|