Stephanny Vicuna Polo
Biographic Data
| ID | 7804315 |
|---|---|
| NAME | Stephanny Vicuna Polo |
| GIVEN NAMES | Stephanny Vicuna |
| FAMILY NAME | Polo |
| SIGNATURE | POLO S V |
| AFFILIATIONS | Al-Quds University |
| ORCID | 0000-0002-8308-6801 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
The association between food preferences, eating behavior, and body weight among female university students in the United Arab Emirates
Introduction This cross-sectional study investigated the associations between lifestyle, eating habits, food preferences, consumption patterns, and obesity among female university students in the United Arab Emirates (UAE). Methods Approximately 4,728 participants, including both Emirati and Non-Emirati students (International Students). Data collection involved face-to-face interviews and anthropometric measurements, showing an interrelated rela…
Investigating the Association between Nutrient Intake and Food Insecurity among Children and Adolescents in Palestine Using Machine Learning Techniques
Food insecurity is a public health concern that affects children worldwide, yet it represents a particular burden for low- and middle-income countries. This study aims to utilize machine learning to identify the associations between food insecurity and nutrient intake among children aged 5 to 18 years. The study's sample encompassed 1040 participants selected from a 2022 food insecurity household conducted in the West Bank, Palestine. The results…
Machine Learning Approach for Predicting the Impact of Food Insecurity on Nutrient Consumption and Malnutrition in Children Aged 6 Months to 5 Years
This study provides insights into the differential risks for growth issues among children, offering valuable information for targeted interventions and policymaking
Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments
The findings can inform evidence-based strategies for preventing and mitigating the detrimental effects of political violence on individuals and communities, highlighting the importance of addressing the needs of children in conflict-affected areas and the potential of using technology to improve their well-being
Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the Covid-19 pandemic
The ML algorithms seem to be an effective method in early detection and prediction of food insecurity and can profoundly aid policymaking. The integration of ML approaches in public health strategies could potentially improve the development of targeted and effective interventions to combat food insecurity in these regions and globally
No prominent works on this page.
Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments
The findings can inform evidence-based strategies for preventing and mitigating the detrimental effects of political violence on individuals and communities, highlighting the importance of addressing the needs of children in conflict-affected areas and the potential of using technology to improve their well-being
Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the Covid-19 pandemic
The ML algorithms seem to be an effective method in early detection and prediction of food insecurity and can profoundly aid policymaking. The integration of ML approaches in public health strategies could potentially improve the development of targeted and effective interventions to combat food insecurity in these regions and globally
The association between food preferences, eating behavior, and body weight among female university students in the United Arab Emirates
Introduction This cross-sectional study investigated the associations between lifestyle, eating habits, food preferences, consumption patterns, and obesity among female university students in the United Arab Emirates (UAE). Methods Approximately 4,728 participants, including both Emirati and Non-Emirati students (International Students). Data collection involved face-to-face interviews and anthropometric measurements, showing an interrelated rela…
Investigating the Association between Nutrient Intake and Food Insecurity among Children and Adolescents in Palestine Using Machine Learning Techniques
Food insecurity is a public health concern that affects children worldwide, yet it represents a particular burden for low- and middle-income countries. This study aims to utilize machine learning to identify the associations between food insecurity and nutrient intake among children aged 5 to 18 years. The study's sample encompassed 1040 participants selected from a 2022 food insecurity household conducted in the West Bank, Palestine. The results…
Machine Learning Approach for Predicting the Impact of Food Insecurity on Nutrient Consumption and Malnutrition in Children Aged 6 Months to 5 Years
This study provides insights into the differential risks for growth issues among children, offering valuable information for targeted interventions and policymaking
Environmental health (5 works) · Medicine (5 works) · Agriculture (3 works) · Biology (3 works) · Food insecurity (3 works) · Food security (3 works) · Food Security and Health in Diverse Populations (3 works) · Geography (3 works) · Child Nutrition and Water Access (2 works) · Malnutrition (2 works)