Developing Predictive Model for Infant Mortality Based on Maternal Determinants and Nutrition Status of 0-59 Month Older Children using a Deep Learning Approach in Ethiopia

1. Developing Predictive Model for Infant Mortality Based on Maternal Determinants and
Nutrition Status of 0-59 Month Older Children using a Deep Learning Approach in Ethiopia
Dawit Shibabaw*
, University of Gondar, Ethiopia, Dep’t of Data science
Email: - dawit.shibabaw@uog.edu.et
Abstract
Deaths of infants between one day and five years old are referred to as infant mortality. According
to the world health organization (WHO) report in 2019, an estimated 5.4 million children under
the age of five are said to have died. This problem is also severe in developing countries like
Ethiopia. According to a federal ministry of health report from 2019, Ethiopia has an estimated
annual death rate of roughly 472,000 children under the age of five, placing Ethiopia sixth globally
in terms of the absolute number of under-five deaths.
As the researcher reviewed different literature, there are some gaps in the research does not conduct
by using deep learning based on maternal detriment and nutrition status. The researcher conducted
data preprocessing techniques accordingly to get the quality of data for model development. In our
study researcher employed four such as, Random forest, an Artificial Neural network, XGBoost,
and a decision tree besides of ANN classifier, among the algorithm’s best accuracy, scored
Random forest with an accuracy of 93.29%. Besides the best-performed algorithm, we deployed
on cloud computing framework on Heroku.
Keywords:- Deep learning, nutrition, maternal determinants, ANN
I. Introduction
Deaths of infants between one day and five years old are referred to as infant mortality[1].
Globally, according to the world health organization (WHO) report in 2019, UNICEF, and World
Bank, an estimated 5.4 million children under the age of five are said to have died[2]. Despite
significant progress over the previous few decades, sub-Saharan Africa continues to have the
highest rate of under-five mortality in the world, accounting for almost half of the total burden,
according to a WHO report from 2021 [2][3]. Following India, Pakistan, Nigeria, and the
Democratic Republic of the Congo, Ethiopia seems to have the fifth-highest rate of infant fatalities
worldwide. According to a federal ministry of health report from 2019, Ethiopia has an estimated

2. annual death rate of roughly 472,000 children under the age of five, placing Ethiopia sixth globally
in terms of the absolute number of under-five deaths[2]. Despite this progress toward achieving
Millennium Development Goal 4 (MDG 4), Ethiopia's under-five mortality rate continues to be
greater than that of many other low- and middle-income nations[4]. Through every stage of life,
nutrition is the most important factor supporting human health and physical growth [5]. Perhaps
the most accurate indicator of a child's well-being is their nutritional status. The causes of
undernutrition in children are numerous and complex [6]. The risk of nutritional deficiency is
higher in children under five than in any other age group[7]. A healthy lifestyle depends on having
a good diet [8]. One of the issues with world health is malnutrition, particularly when it comes to
child survival. Half of all deaths globally among children under five are directly or indirectly
attributable to malnutrition, a major issue in developing countries [8].
I. Related work
Several studies investigated perinatal mortality in Ethiopia using different methods. Dhaka et al
[6], For a developing country like Bangladesh, malnutrition might be seen as a major problem.
Since tomorrow's workforce will be made up of today's children, this directly affects Bangladesh's
economic development [6][9]. Therefore, the most important area of research at present time is the
prevention of childhood malnutrition. The purpose of the project is to categorize malnutrition using
a deep learning technique to predictive modeling on important malnutrition traits to determine a
child's malnutrition status who is between the ages of 0-59 months. To achieve this, the children's
data from the Bangladesh Demographic and Health Survey (BDHS) 2014 are subjected to an
Artificial Neural Network (ANN) technique[6][10]. This study delineates the categories used by a
predictive algorithm to categorize nutritional status. For wasting, underweight, and stunting, the
ANN technique exhibits the highest degree of accuracy. Finally, for both policymakers and
physicians, determining the condition of malnutrition using a deep learning approach is the most
scientific course of action.
In order to identify the nutritional risk variables that are responsible, several statistical techniques
have been examined. Among the techniques, linear regression and logistic regression are
extensively researched for the detection of malnutrition in 0 to 59-month-old children [11] [12]
[13]. Rule induction classifier with receiver operating curve (ROC), Nave Bayes [14], decision
tree [15], and association rules are some of the models that can be used to explain a child's nutrition

3. measurement level. Few studies have employed least squares calculations and variable analysis to
determine the relationship between the chosen factors and malnutrition [16] [17] [18] [19].
As researcher knowledge, there is only study conducted based on either nutritional factors or
maternal determinants to predict infant mortality and identify the risk factors by using statistical
techniques, since statistical techniques its weakness of limited dataset to analysis and do not
extract hidden patterns from the complex dataset. As the researcher’s knowledge, there is no
research conducted by taking nutritional and maternal determinants to predict infant mortality.
II. Methodology
A quick summary of the dataset is given in the next section. The preparation of data and suggested
strategies for doing so using different tools and languages will be discussed in the sections that
follow
A. Data source
The source of raw data was taken from the Ethiopian demographics health survey (EDHS) since
from 2016 and 2019. It has been designed to cover both rural and urban under every division of
Ethiopia country. Among rural and urban areas EDHS consists of a birth record, nutrition record,
maternal record, and household record. Ethiopia’s central statistical agency collects raw data five
years’ intervals and provides research for further analysis. In this study researcher used 16,283 raw
data.
B. Data preprocessing
In data preprocessing researchers, to get the quality of data for model development researchers to
follow the different steps of data preprocessing, filling missing values, outlier detection, removing
redundancy, data transformation, imbalance problem handling, feature selection, and model
deployment. For missing value handling, we used mode and imputation methods since the nature
of the data is categorical. To detect and fill outliers researcher used a box plot and remove them
by using the interquartile range (IQR). To transform data researcher used binning and
discretization techniques. In these studies we have an imbalance in which 87% alive and 13% died,
to handle this problem, according to [20] [21][22], we applied systematic minority over sampling
techniques(SMOTE). After we applied SMOTE have to get data 30,014 datasets. For finally all

4. attributes do not use model development researcher used feature selection, in this study we used
wrapper feature selection techniques which are a step forward and step backward feature selection
applied. The step backward feature section scored the best accuracy of feature selection by taking
18 features and 2 features we recommended by domain experts. We deploy the model on flask
framework python library and front end of HTML on a Heroku cloud platform.
C. Proposed model
Preprocessed data is now prepared to fit the deep learning model. Our deep learning model has
been implemented with the Tensor flow. Data flow graphs are used by Tensor flow to create
models. Building massive layers in an Artificial Neural Network (ANN) is necessary in order to
examine our model. Our model is constructed using "Keras," a well-known package, and Tensor
Flow is utilized to train the factors at the model's backend.

5. Figure 1: Proposed Model Framework
After initializing the artificial network, the model took 20 neurons as features in the input layer
and 10 in the hidden layer. In the proposed model, ANN used three hidden layers after testing

6. gradually one by one. As the researcher proposed to classify infant mortality from trained data, it
sets the range (0, 1) of a linear function in ANN using the rectifier activation function applied in
the hidden layer and sigmoid activation function in the output layer. As 80% of the data is taken
as training data and fits a model which runs 1000-2000 epochs. Here each epoch is considered as
one forward and one backward propagation. Finally the most potent stochastic gradient descent
optimizer parameter “adam” in which a perfect gradient descent algorism is used.
Figure 2: Artificial neural network framework based on Tensor flow
III. Result and discussion
The purpose of the study is to determine being alive and died child from trained data, Random
forest approaches show the best result with an accuracy of 93.29%, and also Decision Tree,
Extreme Gradient Boosting, and ANN, with an accuracy of 91.47%, 91.44%, and 81.91%
respectively. These test have done by 20% of the data with K-fold (k=10). We mainly implemented
ANN on our datasets whereas other machine learning algorithms have been used such as Random
forest, Decision tree, and Extreme Gradient boosting beside of ANN classifier.

7. Evaluation Criteria Algorithms
Random
forest
XGBoost Artificial neural
network
Decision Tree
Accuracy (%) 93.29 91.44 81.91 91.47
Precision (%) 95.43 94.53 79.17 93.86
Recall (%) 90.87 87.88 86.39 88.67
ROC (%) 94.23 90.21 87.36 94.98
Table 1: Overall Algorithms Evaluation
Figure 3: Accuracy of all algorithms
Besides of Best performed algorithm (Random forest by ANN classifier) we have deployed on
Cloud computing framework Heroku, which was designed by the front end by HTML and back
end by python flask framework for potential users. As shown image and link” ”.
IV. Conclusion
The human brainpower may have restricted intellectual ability to predict infant mortality. At the
same time, artificial intelligence can iterate considerable data but that may lack of logical ability.

8. One of the most effective scientific approaches is to use deep learning mechanisms to determine
infant mortality. This strategy may lower the number of deaths, particularly in a developing
country like Ethiopia where a huge number of kids are affected by it. Maternal and child health is
Bangladesh's top priority to achieve the Sustainable Development Goals (SDGs). Consequently,
decision-makers and Healthcare professionals can quickly benefit from the depth learning of how
to foresee infant mortality youngster in advance
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