مشخصات مقاله | |
ترجمه عنوان مقاله | استفاده از الگوریتم یادگیری ماشینی بهترین عملکرد برای پیش بینی مرگ کودک قبل از جشن تولد پنجم |
عنوان انگلیسی مقاله | Using best performance machine learning algorithm to predict child death before celebrating their fifth birthday |
نشریه | الزویر |
انتشار | مقاله سال ۲۰۲۳ |
تعداد صفحات مقاله انگلیسی | ۲۹ صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس میباشد |
نمایه (index) | Scopus – DOAJ |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
۶٫۲۵۵ در سال ۲۰۲۲ |
شاخص H_index | ۴۶ در سال ۲۰۲۳ |
شاخص SJR | ۰٫۷۸۹ در سال ۲۰۲۲ |
شناسه ISSN | ۲۳۵۲-۹۱۴۸ |
شاخص Quartile (چارک) | Q2 در سال ۲۰۲۲ |
فرضیه | ندارد |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | دارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی پزشکی – پزشکی – کامپیوتر |
گرایش های مرتبط | انفورماتیک پزشکی – سایبرنتیک – هوش مصنوعی |
نوع ارائه مقاله |
ژورنال |
مجله | انفورماتیک در پزشکی بدون قفل – Informatics in Medicine Unlocked |
دانشگاه | Mettu University, College of Health Science, Department of Health Informatics, Ethiopia |
کلمات کلیدی | مرگ کودک – پیش بینی – یادگیری ماشینی |
کلمات کلیدی انگلیسی | Child death – Prediction – Machine learning |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.imu.2023.101298 |
لینک سایت مرجع | https://www.sciencedirect.com/science/article/pii/S2352914823001442 |
کد محصول | e17508 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract ۱ Introduction ۲ Research questions ۳ Methods and materials ۴ Study variables ۵ Model building ۶ Model evaluation ۷ Prediction and association rule mining ۸ Results ۹ Discussion ۱۰ Strengths and limitations of the study ۱۱ Conclusions and recommendations Ethical approval and consent to participate Consent for publication Availability of data and materials Patient and public participation Funding Author’s contributions References |
بخشی از متن مقاله: |
Abstract Introduction Methods Results Conclusions
Introduction Under-five mortality is the most important indicator to measure the health status of children, and it is a key marker for the development of countries [1]. The under-five mortality rate is the probability of children dying before their fifth birthday [2]. Globally, nearly 44% of all under-five deaths occurred before their first month of birth [3], and an estimated 4.1 million child deaths occurred in 2017 [4]. According to the Centers for Disease Control and Prevention, child mortality in the United States in 2020 was predicted to be 5.4 deaths per 1000 live births [5].
The risk of under-five mortality is highest in low-income countries. The under-five mortality rate in low-income countries was predicted to be 69 deaths per 1000 live births in 2017, which is almost 14 times the rate in high-income countries [6,7]. In Bangladesh, 522 under-five children died per 1000 live births [8]. In 2001, under-five mortality in Nepal was projected to be 91 deaths per 1000 live births [9]. Though under-five mortality shows a reduction from 166 to 67 per 1000 live births over a period of 16 years [10], Ethiopia appears to have the fifth-highest number of new-born deaths in the world [11]. Under-five mortality is projected to cause 472,000 children to die annually in Ethiopia before their fifth birthday, which places Ethiopia sixth in the world according to the number of under-five deaths [7,12]. According to WHO 2017, more than half of under-five deaths are due to infectious diseases that are easily preventable and treatable through simple and affordable interventions [13]. Under-five mortality is also caused by undernutrition, which further leads to stunting and wasting [14].
Conclusions and recommendations This study aimed to identify the best-supervised machine learning algorithms to classify and select important attributes to predict the death of children before their fifth birthday. In line with the objectives, six supervised machine learning algorithms were considered that accurately predict the death of children before celebrating their fifth birthday. Different confusion matrix element was used to compare the candidate-supervised machine learning algorithms. Based on the result, the random forest algorithm was the best performance model to predict the death of children before celebrating their fifth birthday. Attributes such as late initiation of breastfeeding, mothers with no formal education, the short birth interval, poor wealth status of the mother, and being unexposed to media were the top important attributes to predict child deaths.
Generating associated rules for child death was another objective of the study. Accordingly, six rules were generated that were associated with the deaths of children before celebrating their fifth birthday. The findings of this study would have practical implications by supporting policymakers and stakeholders in developing childcare intervention mechanisms and preparing themselves to care for children as early as possible. Stakeholders are recommended to encourage mothers to initiate breastfeeding at the appropriate time. Improving mothers’ wealth status, closing the gap in media access, and creating awareness among mothers would be critical interventions to enhance the survival of children in Ethiopia. The generated rules would also have theoretical implications by extracting and representing knowledge. Moreover, researchers would use this study as a baseline and framework for further research studies, including important attributes that would predict child mortality in low-income countries. |