https://doi.org/10.31449/inf.v45i1.3111 Informatica 45 (2021) 117–125 117 
 
A Comparative Analysis of Machine Learning Algorithms to Build a 
Predictive Model for Detecting Diabetes Complications 
Ali A. Abaker 
 
Department of Accounting Information
 
Systems, Faculty of Computer Science 
Al-Neelain University, Khartoum, Sudan 
E-mail: aliabdallah@neelain.edu.sd 
 
Fakhreldeen A. Saeed
 
Department of Software Engineering, Faculty of Computer Science, Al-Neelain University, Khartoum, Sudan 
E-mail: fasaeed@neelain.edu.sd 
 
Keywords: diabetes complications, machine learning, logistic regression, electronic health records, feature selection. 
Received: April 4, 2020 
Diabetes complications have a significant impact on patients’ quality of life. The objective of this study 
was to predict which patients were more likely to be in a complicated health condition at the time of 
admission to allow for the early introduction of medical interventions. The data were 644 electronic 
health records from Alsukari Hospital collected from January 2018 to April 2019. We used the following 
machine learning methods: logistic regression, random forest, and k-nearest neighbor (KNN). The 
logistic regression algorithm performed better than the other algorithms achieving an accuracy of 81%, 
recall of 81%, and F1 score of 75%. Also, attributes such as infection years, swelling, diabetic 
ketoacidosis, and diabetic septic foot were significant in predicting diabetes complications. This model 
can be useful for the identification of patients requiring additional care to limit the complications and 
help practitioners in making decisions on whether the patient should be hospitalized or sent home. 
Furthermore, we used the sequential feature selection (SFS) algorithm which reduced the features to six, 
which is fewer than any model built before to predict diabetes complications. The primary goal of this 
study was achieved. The model had fewer attributes which means we have a simple and understandable 
model in addition to, it has a better performance. 
Povzetek: Podana je analiza metod strojnega učenja za napovedovanje komplikacij pri sladkorni 
bolezni. 
 
1 Introduction 
Diabetes is a chronic disease and considered a serious 
global health challenge[1]. Diabetes Complications 
happen when diabetes is uncontrolled. That leads to 
serious health problems; the patient could suffer from a 
diabetic coma or die from a heart attack or stroke. The 
number of people with diabetes has risen to 422 million 
in 2014 which was about 17% of the world 
population[2]. More than 68% of diabetes-related deaths 
are caused by diabetes complications[3]. About 25% of 
people with diabetes are undiagnosed in the United 
States[4]. To address diabetic complications, doctors 
need to be allowed to identify and monitor patients at 
risk of complications[5]; [6]. Early discovery can prevent 
or delay diabetes-related complications and allow for 
effective intervention at both the individual and 
population levels, which are desperately needed to slow 
the diabetes epidemic[7]. 
The objective of this study was to predict which 
patients were more likely to be in a complicated health 
condition at the time of admission, which helps for the 
early introduction of medical interventions. We used the 
following machine learning methods: logistic regression, 
random forest, and KNN. The dataset was collected from 
Alsukari Hospital for building the machine learning 
model. The final model used the most six significant 
attributes and 644 records. This model is useful for 
predicting the likelihood that the patient should be 
hospitalized or sent home. 
The rest of this paper is organized into Sections 
detailed as follows: section 2 literature review, data 
collection in section 3, feature selection in section 4, 
sequential feature selection section 5, model design in 
section 6, evaluation metrics section 7, results in section 
8, section 9 conclusion, study limitations in section 10, 
last future work section 11. 
2 Literature review 
Various traditional methods, based on the physical and 
chemical tests are available for diagnosing diabetes. 
However, methods based on data mining techniques can 
be effectively implemented[8]. The authors agreed on the 
importance of developing machine learning algorithms to 
learn patterns and decision rules from data[9]. Although, 
many studies were conducted to assess the main causes 
of diabetes mellitus. But, a few were directed to discover 
the clinical risk factors[10]. A machine learning model 
was built to predict wound complications and 
mortality[11].    

118 Informatica 45 (2021) 117–125 A.A.Abaker et al.  
 
Electronic health records were used for many studies 
related to diabetes[12]; [13]. A method that enables risk 
assessment from electronic health records(EHR) on a 
large population, they also added administrative claims 
and pharmacy records[14]. Another study proposed a 
model that predicts the severity as a ratio interpreted as 
the impact of diabetes on different organs of the human 
body, the algorithm estimated the severity on different 
parts of the body like the heart and kidney[15]. A rapid 
model for glucose identification and prediction based on 
the idea of model migration[16]. Despite the data was 
collected from different sources and places but, some 
attributes were used in many studies such as age, gender, 
body mass index(BMI), glucose, blood pressure, time of 
diagnosis, and smoking [9]; [17]; [18]; [11]. 
 
Table 1 above is the review of the popular 
algorithms used in diabetes studies related to machine 
learning from 20015 to 2019. And the figure below 
demonstrates the trend. 
 
According to the figure above, logistic regression 
(LR), decision tree (DT), and supervised machine 
learning (SVM) are the most popular algorithms used for 
diabetes studies among the research community with 
about 17%. Followed by, the artificial neural network 
(ANN) with 14%. According to, literature review 
researchers designed about 12% out of all used 
algorithms as new methods for diabetes problems.  
We built a machine learning model to predict 
diabetes complications, using six attributes. This model 
introduced new attributes such as diabetic ketoacidosis, 
swelling, infection years, and diabetic septic foot were 
found to be significant. These attributes were not 
included in the previously mentioned studies[30]; [13]; 
[31]; [9]. Also, in this paper, we investigated five 
performance metrics such as F1 and recall. 
A logistic regression model was used to assess the 
factors associated with glycemic control. The model 
indicated that patients older than 65 years old were more 
likely to have complications compared to the 
younger[32]. Demographic and treatment data were 
collected and logistic regression was used to predict 
complications[33]. Another study was conducted to 
predict 30-day complication rate using random forest and 
logistic regression, the analysis showed that age is the 
most significant attribute in predicting 
complications[34]. Random forest and simple logistic 
regression methods showed the best performance 
compared to the evaluated algorithms[35]. Diabetes 
complications prediction model was based on similarity 
measure. first, they assessed the similarity between 
textual medical records after data cleaning, then topic 
mining is conducted, and last building the model[36]. 
3 Data collection 
The dataset was collected from Alsukari Hospital. 
Ethical approval to use the data for research was obtained 
both from the Ministry of Health (MOH) and the 
hospital. The dataset contained 29 attributes and 644 
records of diagnosed diabetes patients who were 
admitted to the hospital in the period from January 2018 
to April 2019. 
4 Feature selection 
Classification problems usually have a big number of 
features in the dataset, but not all of them are significant 
for classification. Irrelevant features may reduce the 
performance and even complicate the model. Feature 
selection aims to select a small number of relevant 
features to get similar or better classification 
performance than using a larger number of features[39].  
Thus, it is usual to apply a preprocessing step to 
remove irrelevant features and reduce the dimensionality 
of the data[40]. The selection of the features can lead to 
an improvement of the learning algorithm, either in terms 
of learning speed, generalization, or simplicity of the 
model. Furthermore, there are other advantages 
associated with a reduced feature: low cost, clear model, 
and a better understanding of the domain knowledge[41].  
The figure below illustrates the five approaches for 
feature selection. 
 
Figure 2: Shows feature selection approaches. 
Approaches of Feature Selection
Filter Wrapper Hybrid Embedded Ensemble
Algorithms 2015 - 2019 
ANN [8],[19],[2],[20],[21],[11] 
K-means [22] 
Logistic [22],[9],[2],[20],[23],[24],[11] 
Decision Tree [1],[20],[3],[25],[24], [10],[26] 
SVM [27],[2],[1],[20],[3],[21],[24] 
KNN [1],[21],[24] 
Random Forest [1],[24] 
Naive Bayes [3],[23],[25],[24] 
Designed 
algorithms 
[28],[13],[5],[29],[9] 
Table 1: Shows the trends of used algorithms in previous 
literature. 
 
Figure 1: Shows the trend of algorithms used in the last 
five years. 
 

A Comparative Analysis of Machine Learning Algorithms... Informatica 45 (2021) 117–125 119 
 
4.1 Filter method 
Filter based feature methods evaluate features as an 
individual assessment. Therefore, these methods first 
assign a score value for each feature using one of the 
statistical criteria, and then, all features are sorted 
according to the scores. Then, they select top-n features 
with the highest score as the final step[42]. Filter feature 
selection algorithms are useful due to their simplicity and 
fast speed. A common filter is to use mutual information 
to evaluate the relationships between each feature and the 
class variable[43]. 
4.2 Wrapper method 
Feature selection algorithms are divided into two 
methods. Firstly, it depends on the outcome of the 
selection algorithm: whether it returns a subset of 
significant features or an ordered ranking of all the 
features, recognized as feature ranking. Secondly, feature 
selection methods are divided into three approaches 
based on the relationship between a feature selection 
algorithm and the learning method, which is used for 
building the model: filters, which rely on overall 
characteristics of the dataset and are independent of the 
learning algorithm; wrappers, which use the prediction of 
a classifier to estimate subsets of features; and embedded 
methods, which perform the selection in the process of 
training and are specific to the given learning algorithm. 
Wrapper techniques depend on the classification 
algorithm, which is used to evaluate the subsets of 
features, but they are more expensive when it comes to 
computation[44]. Despite this weakness, they often 
provide better outcomes; wrappers are used widely in 
many applications[45]. Especially, in healthcare where 
we care about the accuracy more than the performance of 
the algorithm in many situations and allow for 
implementation in real-time systems when we have fewer 
attributes[46]. 
4.3 Hybrid method 
Recently, researchers are concentrating on developing 
novel hybrid feature selection methods as they speed up 
the removal of irrelevant features and give greater 
classification accuracy compared to other methods[47]. 
Though various techniques were developed for selecting 
the perfect subset of features, these methods faced some 
problems such as instability, high processing time, and 
selecting a semi-optimal solution as a final result. In 
other words, they have not been able to fully extract the 
effective features. Hybrid methods were introduced as a 
solution to overcome the weaknesses of using a single 
algorithm[48]. 
4.4 Embedded method 
Embedded feature selection is related to classification 
algorithms. This relation in embedded methods is 
stronger than that in wrapper methods. Embedded 
methods are a sort of combination of filter and wrapper 
methods[49]. By, embedding feature selection into the 
model learning. They return both the learned model and 
selected features and are frequently used for 
classification[50]. Inserting the feature selection step into 
the training process can improve the performance of the 
model. 
4.5 Ensemble method 
Ensemble learning is an effective method for machine 
learning. The objective is to attain better learning 
accuracy by combining different learning models[41]. 
Ensemble methods are better than using a single machine 
learning model. Recently, the development of ensemble 
feature selection is increasingly getting attention[51]. 
Attribute Type  Scale Description 
Infection 
years 
Continuous 0 – 35 Is the period 
from the 
patient was 
diagnosed 
with Diabetes 
Sugar Continuous 53 -
681 
Blood sugar or 
glucose level 
in the body is 
measured in 
mg/dL or 
milligrams per 
deciliter. 
Swelling Category  Binary Swollen 
body part. 
DKA Category  Binary Diabetic 
ketoacidosis 
called 
[22]ketones. 
DSF Category  Binary Diabetic 
septic Foot is 
a foot affected 
by ulceration 
of the lower 
limb in a 
patient with 
diabetes[37].  
HR Continuous 27 -
139 
Is the speed of 
the heartbeat 
measured by 
the number of 
contractions 
(beats) of the 
heart per 
minute 
(bpm)[38]. 
 
Class Binary  Binary The Target 
variable. 
Table 2: Shows the selected attributes and their 
descriptions to build the model. 

120 Informatica 45 (2021) 117–125 A.A.Abaker et al.  
 
Combined feature selection aims to find multiple optimal 
features. The advantage of integration technology would 
produce a stable and efficient method; especially, with 
high-dimensional data[52]. 
5 Sequential feature selection (SFS)  
The sequential feature selection(SFS) algorithm begins 
with a blank set and increases one feature for the first 
step which gives the best value for the model. On the 
second step onwards the remaining features are added 
separately to the existing subset and the new subset is 
assessed. The new feature is permanently added to the 
subset if it gives the maximum classification accuracy. 
The process is repeated until the required number of 
features are added[53]. SFS method has the advantage of 
improving the prediction performance of the classifier by 
excluding any characteristic that reduces the 
performance[54]. 
6 Model design 
Working with methods for reasoning under uncertainty is 
now one of the most interesting areas of machine 
learning [20]; [21]. Machine learning has been used for 
several decades to tackle a broad range of problems in 
many fields of applications[57]. The machine learning 
model was built on the historical data using different 
algorithms for each model; we evaluated the results, and 
assessed the model accuracy on the testing data. Six 
attributes were selected out of 29. The dataset was 
divided into two parts: training and testing set consisting 
of 70 and 30 percent respectively. The features were 
selected to be used in the final model based on the 
training set which achieved the highest accuracy of 86%, 
with the six attributes as shown in table 2. Logistic 
regression, random forest, and KNN were selected 
because of their simplicity and good predictive 
capability. However, machine learning models are more 
accurate than normal statistical methods[58].  
6.1 Logistic regression 
Logistic regression models are a sort of widespread 
linear model that is used for datasets where the 
dependent variable is categorical[59]. The logistic model 
is used to estimate the probability of the response 
variable based on one or more predictor variables. 
Logistic regression is used when the dependent variable 
is categorical and generates output in terms of 
probabilities[60]. Logistic regression is an effective 
prediction algorithm. Its applications are efficient when 
the dependent variable of a dataset is binary[61]. 
𝑓 (𝑥 ) =
𝐿 1+𝑒 −𝑘 (𝑥 −𝑥 0
)
                 (1) 
Where 
X 0 = the x value of the sigmoid’s midpoint. 
L = the curve’s maximum value. 
K = the logistic growth rate or steepness of the 
curve. 
6.2 Random forest 
Random Forest was proposed by Dr. Breiman in 2001. It 
is typically used in classification[62]. It is an algorithm 
based on statistical learning theory, which uses a 
bootstrap randomized re-sampling method to extract 
multiple versions of the sample sets from the original 
training datasets. Then it builds a decision tree model for 
each sample set, and finally combines all the results of 
the decision trees to predict via a voting mechanism[63].  
Suppose we have the dataset D = {(x1, y1) … (xn, 
yn)} and the aim is to find the function f : X →Y where 
X is the inputs and Y is the produced outputs. 
Furthermore, let M be the number of inputs. Random 
forest randomly selects n observations from D with 
replacement to a bootstrap sample. Each tree is grown 
using a subset of m features from the overall M features. 
For regression, it is recommended to set the subset of 
features at M=3. Then at each node, m features are 
nominated at random and the best performing split 
among the M features is selected according to the 
impurity measure (Gini impurity). The trees are grown to 
a maximum depth without pruning[64]. 
6.3 K-nearest neighbor (KNN)  
It is a classification algorithm that classifies data based 
on similarity measure or distance measure[18]. This 
algorithm can be used in both classification and 
regression problems[21], [65]. KNN classifies an 
instance by finding its nearest neighbors[66]. The KNN 
classifier applies the Euclidean distance or cosine 
similarity for differentiating the training tuple and test 
tuples. The same Euclidean distance between tuples Xi 
and Xt (t = 1,2,3 …n) can be explained as[67]: 
𝑑 (𝑦𝑖 , 𝑦𝑡 ) = √(𝑦 𝑖 1 
− 𝑦 𝑡 1
)
2
+ (𝑥 𝑖 2
− 𝑥 𝑡 2
)
2
+ ⋯ + (𝑥 𝑖𝑠
− 𝑥 𝑡𝑠
)
2
                     (2) 
 
Figure 3: Shows logistic regression curve. 

A Comparative Analysis of Machine Learning Algorithms... Informatica 45 (2021) 117–125 121 
 
where yi, n and s be the tuple constant. It can be written 
as: 
𝐷𝑖𝑠𝑡 (𝑦 1, 𝑦 2 = √∑(𝑦 1𝑖 
𝑛 𝑖 =1
− 𝑦 2𝑖 )
2
              (3) 
This equation is prepared based on KNN algorithms, 
every neighboring point that is closest to the test tuple, 
which is encapsulated and on the nearby space to the test 
tuple[68]. 
7 Evaluation metrics 
The three algorithms, Logistic Regression, Random 
Forest Classifier, and KNN were compared in terms of 
accuracy, recall, specificity, precision, and F1scores as 
demonstrated below. The level of efficiency of the 
classification model is measured with the number of 
correct and incorrect classifications in each potential 
value of the variables being classified. From the 
outcomes gained. The following equations are used to 
measure the Accuracy, Sensitivity, and Specificity, 
Precision, and F1 score [69]. 
Accuracy = 
𝑇𝑃 +𝑇𝑁
𝑇𝑃 +𝑇𝑁 +𝐹𝑃 +𝐹𝑁
                  (4) 
Recall or also known as sensitivity refers to the 
percentage of total relevant results correctly classified by 
your algorithm. 
Recall     =       
𝑇𝑃
𝑇𝑃 +𝐹𝑁
                   (5) 
Specificity is defined as the proportion of actual 
negatives, which got predicted as the negative (or true 
negative). 
Specificity = 
𝑇𝑁
𝑇𝑁 +𝐹𝑃
                          (6) 
The precision of all the records we predicted 
positive. 
Precision = 
𝑇𝑃
𝑇𝑃 +𝐹𝑃
                           (7) 
F1-score, which is simply the harmonic mean of 
precision and recall. 
F1 Score = 
2𝑇𝑃 +𝑇𝑁
2𝑇𝑃 +𝐹𝑃 +𝐹𝑁
                                  (8) 
From table 3 above the logistic regression achieved 
the highest accuracy of 81%. According to the medical 
objective, the model was designed to be more sensitive in 
predicting true positive which was calculated by recall 
and F1 of 81 and 79 percent respectively. As this model 
is used in healthcare our interest in predicting the 
positive class, which is more important. It is acceptable 
for the model to fall into a false positive error (type 1 
error). But it is very costly for the model to commit the 
(false negative error) type 2 error, it means that the 
patient might be in complicated health status and needs 
special and immediate medical care and intervention, but 
the model tells us that the patient will not be in a 
complicated situation. And that could result in more 
health complications and even the patient’s life. 
8 Results 
New attributes were found to be significant in predicting 
diabetes complications such as infection years, swelling, 
diabetic ketoacidosis, diabetic septic foot, which were 
found to be vital in predicting diabetes complications. 
 
Figure 4: Shows the model selection process. 
Algorithm/
Metrics 
Accura
cy 
Recall 
Specifi
city 
Precisio
n 
F1 
Logistic 
Regression 81% 81% 81% 70% 75% 
Random 
Forest 
Classifier 
78% 57% 89% 74% 64% 
KNN 
76% 62% 84% 68% 65% 
Table 3: Shows the three algorithms and their metrics 
scores using the default threshold. 
 
Figure 5: Shows model performance metrics. 

122 Informatica 45 (2021) 117–125 A.A.Abaker et al.  
 
But, blood pressure, body temperature, cholesterol, 
protein level, oxygen level, and gender were not 
significant in predicting diabetes complications because 
they were excluded by the feature selection algorithm. 
We compared the following three algorithms: logistic 
regression, random forest classifier, and KNN to find the 
best algorithm for predicting diabetes complications. 
Accuracy, recall, specificity, precision, and F1; used as 
performance metrics. These metrics were used for 
selecting the best model. The logistic regression 
algorithm achieved the highest recall score of 81%, 
followed by KNN and random forest of 62% and 57% 
respectively, as shown in Table 3 above. This means that 
the model is more sensitive in predicting the positive 
class. Also, the model achieved the highest F1score of 
75% as shown in Table 3 above. The model was 
designed to be more sensitive in predicting true positive 
class which means the diabetes status is complicated. It  
was calculated by the recall score of 81%. As this model 
is used in healthcare our interest in predicting the 
positive class, which is significant. 
9 Conclusion 
In this paper, we created a dataset from Alsukari Hospital 
for building our machine learning model. The best model 
was built using six out of 29 attributes. Three algorithms 
were compared in selecting the best model as follows: 
Logistic regression, random forest, and KNN. 
Furthermore, new attributes were investigated and 
included in the model. Finally, the best accuracy was 
obtained using logistic regression. The overall accuracy 
does not guarantee that the model will perform better and 
serve the specific domain interest. According to the 
medical objective, the recall score was used besides 
general accuracy. The higher recall score indicates that 
the model is more sensitive in predicting positive cases 
or medically patients with diabetes complications. 
10 Study limitations  
The accuracy is not very high as we are working in the 
medical field higher accuracy is needed. Second, the size 
of the dataset is small; machine learning models need 
more data for producing stable and well-trained models.  
11 Future work 
There are several future research directions. Firstly, for 
predicting diabetes complications more features could be 
included which were not included in this study. 
Secondly, more work should be directed toward 
identifying the risk factors associated with diabetes 
complications. Last we are interested in adopting this 
model to other chronic diseases. 
12 Acknowledgement 
The authors would like to thank the Ministry of Health 
(MOH) and Alsukari Hospital for giving us the ethical 
approval to use the data for this work. Also, we would 
like to take this opportunity to thank the reviewers for 
their constructive comments towards improving our 
manuscript. 
13 Conflict of interest 
The authors declare no conflict of interest. 
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