دانلود مقاله استفاده از الگوریتم یادگیری ماشینی بهترین عملکرد برای پیش بینی مرگ کودک

خلاصه
1. معرفی
2. سؤالات تحقیق
3. روش ها و مواد
4. متغیرها را مطالعه کنید
5. ماکت ساختمان
6. ارزیابی مدل
7. پیش بینی و تداعی قواعد کاوی
8. نتایج
9. بحث
10. نقاط قوت و محدودیت های مطالعه
11. نتیجه گیری و پیشنهادات
تایید اخلاقی و رضایت برای شرکت
رضایت برای انتشار
در دسترس بودن داده ها و مواد
مشارکت بیماران و مردم
منابع مالی
مشارکت های نویسنده
منابع

Abstract
1. Introduction
2. Research questions
3. Methods and materials
4. Study variables
5. Model building
6. Model evaluation
7. Prediction and association rule mining
8. Results
9. Discussion
10. Strengths and limitations of the study
11. 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

چکیده
معرفی
موربیدیتی و مرگ و میر کودکان در محیط های محدود از منابع یک مشکل عمده بهداشت عمومی است. مطالعات قبلی عمدتاً به تعیین میزان شیوع مرگ و میر کودکان و شناسایی عوامل مرتبط با آن مربوط می شد. استخراج دانش و کشف بینش از الگوهای پنهان در داده های کودک از طریق الگوریتم های یادگیری ماشینی نظارت شده محدود است. بنابراین، این مطالعه با هدف پیش‌بینی مرگ کودکان زیر پنج سال با استفاده از بهترین الگوریتم یادگیری ماشینی با نظارت بر عملکرد انجام شد.

مواد و روش ها
در مجموع 1813 نمونه از مجموعه داده دموگرافیک و سلامت اتیوپی در سال 2019 استفاده شد. 70% و 30% از کل نمونه‌ها به ترتیب برای آموزش مدل و اندازه‌گیری عملکرد هر الگوریتم با تکنیک‌های اعتبارسنجی متقاطع 10 برابری استفاده شد. پنج الگوریتم یادگیری ماشین نظارت شده برای ساخت و مقایسه مدل در نظر گرفته شد. همه الگوریتم های وارد شده با استفاده از عناصر ماتریس سردرگمی ارزیابی شدند. ارزش افزایش اطلاعات برای انتخاب ویژگی های مهم برای پیش بینی مرگ و میر کودکان استفاده شد. ارتباط منطقی If/then برای ایجاد قوانین مبتنی بر روابط بین ویژگی ها با استفاده از نرم افزار Weka نسخه 3.8.6 استفاده شد.

نتایج
J48 دومین الگوریتم بهترین عملکرد در کنار جنگل تصادفی برای پیش‌بینی مرگ کودکان به ترتیب با دقت 77.8 و 93.9 درصد است. شروع دیرهنگام شیردهی، مادران بدون تحصیلات رسمی، فواصل کوتاه تولد، وضعیت ضعیف ثروت مادر و عدم مواجهه با رسانه ها پنج ویژگی مهم برای پیش بینی مرگ و میر کودکان بودند. در مجموع شش قانون مرتبط ایجاد شد که می تواند میزان مرگ و میر کودکان را تعیین کند. از این تعداد، اگر کودکان روستانشین بودند، فاصله تولد کوتاهی داشتند و در صورت تولد چندقلو (دوقلو)، احتمال مرگ کودک 83.6 درصد بود.

نتیجه گیری
پنج الگوریتم یادگیری ماشین برای پیش‌بینی مرگ کودکان و ایجاد قوانین گنجانده شد. از این رو الگوریتم جنگل تصادفی بهترین الگوریتم برای پیش بینی مرگ و میر کودکان بود. با این حال، مطالعه محدود بود زیرا ویژگی‌های مهم در منبع داده گنجانده نشد و مقادیر نامربوط یافت شد. بنابراین، محققان تشویق می شوند از الگوریتم های یادگیری ماشینی برای مطالعات آینده از جمله ویژگی های مهمی که می توانند مرگ کودک را پیش بینی کنند، استفاده کنند. یافته های فعلی برای آمادگی ذینفعان و انجام مداخلات پیشگیرانه در مراقبت از کودک مفید خواهد بود. تشویق زنان به آموزش، دسترسی به رسانه ها و برنامه های توسعه اقتصادی مداخلات ضروری برای کاهش مرگ و میر کودکان است.

Abstract

Introduction
Child morbidity and mortality in resource-limited settings is a major public health problem. The previous studies were mainly concerned with determining the prevalence of child deaths and identifying associated factors. Extracting knowledge and discovering insights from hidden patterns in child data through supervised machine learning algorithms is limited. Therefore, this study aimed to predict the under-five death of children using a best performance-supervised machine learning algorithm.

Methods
A total of 1813 samples were used from the 2019 Ethiopian Demographic and Health Survey dataset. 70% and 30% of total instances were used for training the model and measuring the performance of each algorithm with 10-fold cross-validation techniques respectively. Five supervised machine learning algorithms were considered for model building and comparison. All the included algorithms were evaluated using confusion matrix elements. Information gain value was used to select important attributes to predict child deaths. The If/then logical association was used to generate rules based on relationships among attributes using Weka version 3.8.6 software.

Results
J48 is the second-best performance algorithm next to the random forest to predict child death, with 77.8% and 93.9% accuracy, respectively. Late initiation of breastfeeding, mothers with no formal education, short birth intervals, poor wealth status of the mother, and unexposed to media were the top five important attributes to predict child deaths. A total of six associated rules were generated that could determine the magnitude of child deaths. Of these, if children were rural residents, had a short birth interval, and if born as multiples (twins), then the probability of child death was 83.6%.

Conclusions
Five machine learning algorithms were included to predict child deaths and generate rules. Hence, the random forest algorithm was the best algorithm to predict child deaths. However, the study was limited since important attributes were not included in the data source, and irrelevant values were found. So, researchers are encouraged to use machine learning algorithms for future studies including important attributes that could predict child death. The current findings would be useful for stakeholders’ preparedness, and taking proactive childcare interventions. Encouraging women in education, media access, and economic development programs are essential interventions for child death reduction.

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.