=Paper=
{{Paper
|id=Vol-3126/paper28
|storemode=property
|title=Comparison of machine learning methods for a diabetes prediction information system
|pdfUrl=https://ceur-ws.org/Vol-3126/paper28.pdf
|volume=Vol-3126
|authors=Shmatko Olexandr,Olha Korol,Andrey Tkachov
}}
==Comparison of machine learning methods for a diabetes prediction information system==
https://ceur-ws.org/Vol-3126/paper28.pdf
Comparison of Machine Learning Methods for a Diabetes
Prediction Information System
Olexandr Shmatko 1, Olha Korol 2, Andrey Tkachov3, Vasyl Otenko4
1
National Technical University "Kharkiv Polytechnic Institute" st. Kirpichova, 2, Kharkiv, 61000, Ukraine
2,3,4
Simon Kuznets Kharkiv National University of Economics, ave. Nauki, 9-A, Kharkiv, 61166 Ukraine
Abstract
Diabetes is a disease for which there is no permanent cure; therefore, methods and information
systems are required for its early detection. This paper proposes an information system for
predicting diabetes based on the use of data mining methods and machine learning (ML)
algorithms. The paper discusses a number of machine learning methods such as decision trees
(DT), logistic regression (LR), k-Nearest Neighbors (k-NN). For our research, we used the Pima
Indian Diabetes (PID) dataset collected from the UCI machine learning repository. The dataset
contains information about 768 patients and their corresponding nine unique attributes.
Research has been carried out to improve the prediction index based on the Recursive Feature
Elimination method. We found that the logistic regression (LR) model performed well in
predicting diabetes. We have shown that in order to use the created model to predict the
likelihood of diabetes mellitus with an accuracy of 78%, it is necessary and sufficient to use
such indicators of the patient's health status as the number of times of pregnancy, the
concentration of glucose in the blood plasma during the oral glucose tolerance test, the BMI
index and the result of the calculation. heredity functions "DiabetesPedigreeFunction".
Keywords 1
Machine learning, Data Mining, Neural Network, Diabetes Prediction Information System,
KNN, Logistic regression, Decision tree.
1. Introduction Normally, the level of glucose in the blood
varies within fairly narrow limits: from 3.3 to 5.5
mmol / liter. This is due to the fact that in a healthy
Diabetes mellitus is an "epidemic of the XXI
person, the pancreas produces or stops the release
century", an incurable disease of the pancreas,
of insulin depending on the actual level of glucose
which develops due to absolute or relative
in the blood. In case of insufficiency or complete
insufficiency of the hormone insulin. It is
absence of insulin (type 1 diabetes mellitus) or in
characterized by a steady rise in blood glucose
case of impaired interaction of insulin with cells
levels, which in turn can lead to complications.
(type 2 diabetes mellitus), glucose accumulates in
To achieve compensation for diabetes,
the blood in large quantities, and the body's cells
constant monitoring is required. In addition to
are unable to absorb it. As of 2019, in addition to
taking oral medications and insulin, following a
the already mentioned type 1 and type 2 diabetes,
strict diet, exercise, daily routine, checking your
there are gestational diabetes (gestational
blood glucose regularly, and keeping a special
diabetes), MODY-diabetes and LADA diabetes
diary, your diabetic should see an endocrinologist
[2].
regularly for advice and appropriate measures to
improve or maintain the condition.
ISIT 2021: II International Scientific and Practical Conference
«Intellectual Systems and Information Technologies», September
13–19, 2021, Odesa, Ukraine
EMAIL: oleksandr.shmatko@khpi.edu.ua (A. 1);
korol.olha2016@gmail.com (A. 2); andrew.tkachov@hneu.net (A.
3); ovi@hneu.edu.ua (A. 4)
ORCID: 0000-0002-2426-900X (A. 1); 0000-0002-8733-9984 (A.
2); 0000-0003-1428-0173 (A. 3); 0000-0002-5979-1084 (A. 4)
©️ 2021 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
Depending on the specifics of the diagnosis, predict diabetes. The model proposed in this paper
treatment of patients with diabetes involves the showed a fairly good forecast with an accuracy of
use of oral agents to improve insulin permeability 75.32%. In the study [5], all patient data were used
to body tissues and / or replacement therapy with to train and test a classifier based on Naive Bayes
subcutaneous insulin injections of varying (NB) and decision trees (DT). The research results
duration to mimic the normal functioning of the showed that the best algorithm is the naive
pancreas. With mild diabetes, you can do without Bayesian algorithm with an accuracy of
medication, but a strict diet with a clear 76.3021%.
understanding of the amount of nutrients The most important problem in a machine
consumed, moderate exercise, daily routine, learning method is the choice of training
blood glucose control and diary of self- parameters and the corresponding classifier. In
monitoring are mandatory for all patients with this our work, we used the Recursive Feature
diagnosis. Elimination method to improve the prediction
Under conditions of poor or insufficient rate. Our research work is to select the best
treatment (decompensation or subcompensation classifier for the diabetes prediction information
of diabetes), blood glucose levels in the human system. In this work, various machine learning
body are consistently high. Against this classification algorithms are used to predict
background, complications of diabetes develop, diabetes in a patient, such as Linear Regression
which not only worsen the patient's standard of (LR), K-Nearest Neighbor (KNN), Decision Tree
living, but can also be fatal. These complications (DT).
include:
ketoacidosis (accumulation of a dangerously 3. System design
large number of ketone bodies in the blood),
hypoglycemia (decrease in blood glucose below
The system architecture for the Diabetes
3.3 mmol / l), hyperosmolar and lactic acidotic
Prediction System, shown in Figure 1 below, is a
coma, polyneuropathy (peripheral nerve damage),
nephropathy (kidney damage), retina retinal conceptual model that defines the structure,
behavioral interactions, and several systemic
vessels), angiopathy (impaired vascular
permeability), diabetic foot syndrome, etc. representations that underlie the system. The
To achieve compensation for diabetes - a figure shows a formal description of the system,
condition in which the patient has achieved stable submodules of the system, as well as data flows
normal blood glucose levels during treatment and between them.
the risk of complications is reduced - constant Figure 1 shows the components of the system
monitoring is required. In addition to the above architecture.
measures, this control also includes regular visits
to the endocrinologist for advice and appropriate
measures to improve or maintain the patient's
health.
2. Literature review
There are a number of studies on predicting
diabetes based on machine learning (ML)
methods for the Pima Indian Diabetes Dataset
(PIDD). Pima Indian Diabetes Dataset (PIDD)
containing: 9 attributes, 768 records describing
female patients. [1], [2], [3], [4], [5].
In [2], artificial neural networks were used to
predict diabetes based on the PIDD dataset, which
showed a prediction accuracy of 75.7%. Sajida
The authors of [3] showed that among the applied
machine learning methods SVM, NB and DT on
PIDD, the NB classifier shows the best accuracy - Figure 1: System architecture.
76.30%. [4] applied logistic regression to PIDD to
4. Methods 4.1 An example of solving the
problem of classification using
Based on the comparison and analysis of the
functional properties of leading software solutions
machine learning to predict the
in the field of medicine, it was determined that the incidence of diabetes
option "Obtaining prediction of the probability of
the patient's disease" is not implemented in 4.1.1. Description of the source data
modern diabetes management information
systems. However, due to statistics on the fate of
To implement the considered methods of
patients with misdiagnosis, it becomes impossible
classification of machine learning, we will use the
to deny the need to implement this useful
popular service "UCI Machine Learning
function.
Repository", which provides a large number of
The problem of predicting the incidence of
sets of real data, and consider the initial data
diabetes can be solved using the methods of
presented in the sample "Pima Indians Diabetes
classification of machine learning.
Database" (figure 2)
In the tasks of medical diagnostics, patients act
as objects. The characteristic description of the
patient is, in fact, a formalized medical history.
Having accumulated a sufficient number of
precedents in electronic form, you can use the
methods of classification of machine learning and
predict the likelihood of the patient's disease.
Figure 2: Example data in the Pima Indians Diabetes Database sample
There are a total of 768 records in the sample, 9. "Outcome" - the result of a variable class
each of which is characterized by the following (0 - no diabetes, 1 - a sick person)
nine parameters. The available data show the following
1. "Pregnancies" - the number of times of distribution: 500 people are healthy (ie their
pregnancy "Outcome" parameter is zero) and 268 have
2. "Glucose" - plasma glucose concentration diabetes (their "Outcome" parameter is equal to
(in mg / dl) for two hours in an oral glucose one).
tolerance test In graphical form, the data "Pima Indians
3. "BlooodPressure" - diastolic blood Diabetes Database" can be represented as follows
pressure (in mm Hg) (figure 3).
4. "SkinThickness" - the thickness of the As can be seen from Figure 3, inaccurate data
folds of the skin of the triceps (in mm) are found in the sample. For example, these are:
5. "Insulin" - the concentration of serum 1. blood pressure equal to zero (35 cases);
insulin for two hours (in μg / ml) 2. zero blood glucose concentration (5
6. "BMI" - body mass index, calculated by cases);
the formula: weight in kg / (height in m) 2 3. skin fold thickness less than 10 mm (227
7. "DiabetesPedigreeFunction" - a function cases);
of diabetes heredity 4. BMI approaching zero (11 cases);
8. "Age" - the age of man 5. zero level of insulin concentration in the
blood (374 cases).
Figure 3: Graphic representation of data distribution
To eliminate the above problems, the task of predicting the incidence of diabetes, it is
following options are proposed: advisable to choose the method whose accuracy in
Delete or ignore records. An undesirable the selected sample will be the highest.
option, because it means the loss of valuable Avoidance of training and testing on the same
information. The sample contains too many data is a common practice, which is explained by
records with zero skin thickness and blood the fact that the purpose of the model is to provide
insulin concentration, but this tool can be data other than the sample. In addition, the model
applied to the fields "BMI", "Glucose", "Blood can be overly complex, leading to retraining. To
pressure". avoid the above problems, there are two
Using averages. This option may be the precautionary methods:
case for some samples, but using a mean value, • retention method - part of the training set can
such as blood pressure, will be the wrong be postponed and used as an affirmative (test)
signal for the model. set;
Avoid the use of problematic • cross-checking - repeating the method of
characteristics. This option could work for the retention several times, ie repeating the
thickness of the skin, but at this stage it is division of the sample into training and
difficult to predict the result. approval sets.
After analyzing possible ways to solve the Calculations of the accuracy values of the
problem of incomplete data, we decide to remove selected classification methods will be performed
from the sample rows in which the attributes using Python programming language, namely
"BMI", "Glucose" and "Blood pressure" are zero. using the methods of the library "scikit-learn"
As a result, 724 records remain in the database. [39]. As input parameters "x" we will give models
data from columns "Pregnancies", "Glucose",
"Blood Pressure", "Skin Thickness", "Insulin",
4.1.2. Choice of classification method "BMI", "Diabetes Pedigree Function" and "Age".
As the expected result "y" - data from "Outcome".
In order to choose the method of classification
of machine learning, which is better suited for the
The results of the calculations presented in the identify which of the available attributes have a
table 1. greater impact on the resulting model, we use the
method of recursive Feature Elimination (RFE).
Table 1 The essence of the method is that it recursively
The results of calculating the accuracy of the removes attributes and builds models based on
classification methods by the method of those attributes that remain. RFE uses model
retention and the method of cross-checking accuracy to determine which attributes or
combinations of them contribute most to target
Method retention cross-
prediction.
method checking
Using the library "scikit-learn" we build a
KNN 0.711521 0.711521 graph of the accuracy of the prediction of diabetes
LR 0.776440 0.776440 from the number of initial parameters (Fig. 5).
DT 0.681327 0.685494
80,0
78,0
76,0
74,0
72,0
70,0
68,0
66,0 Figure 5: Dependence of accuracy of prediction
of diabetes mellitus by logistic regression method
64,0 on the number of initial parameters
62,0
KNN LR DT
As you can see, the best accuracy of the model
is achieved by using only four attributes:
retention method cross-checking "Pregnancies", "Glucose", "BMI",
"DiabetesPedigree Function". A comparison of
Figure 4: Comparison of the obtained results of the results of the accuracy calculation is given in
accuracy of classification methods the following table 2/
Based on the obtained data, it can be stated that Table 2
the method of logistic regression has a higher The results of calculating the accuracy of the
accuracy of the sample "Pima Indians Diabetes method of logistic regression after reducing the
Database" than the method of kNN-classification
number of output attributes
and the method of the decision tree (figure 4), and
therefore it can be used to implement the function Number of 8 4
"Diabetes prediction" in the information system to output
automate the process of admission of patients with parameters
diabetes in endocrinologists. Accuracy of
logistic
0.776440 0.780588
4.1.3. Improving the accuracy of regression
method
prediction
Usually, not all source data improve the
performance of the model. In order to correctly Thus, to further use the created model to
predict the probability of diabetes with an
accuracy of 78%, it is necessary and sufficient to [5] Zou, Quan, et al. "Predicting diabetes
use such indicators of the patient's health as the mellitus with machine learning techniques."
number of pregnancies, plasma glucose Frontiers in genetics 9 (2018): 515.
concentration in the oral glucose tolerance test,
BMI and the result of calculating the heredity
function "DiabetesPedigreeFunction".
5. Conclusions
Early detection of diabetes is one of the major
health problems. This paper proposes a system
architecture and classifier for an information
system that can predict diabetes with high
accuracy. We have pre-processed the data. Using
the method of reducing the number of functions,
we have abandoned four parameters. We used
four input parameters ("Pregnancies", "Glucose",
"BMI", "DiabetesPedigree Function") and one
output parameter (result) in the PIMA dataset. We
used three different machine learning algorithms,
including DT, KNN, LR on PIDD, to predict
diabetes and evaluated the performance on
various parameters. All models show good results
in some parameters. All models provided over
70% accuracy. LR provided an accuracy of
approximately 77–78%. The use of improving the
prediction index based on the Recursive Feature
Elimination method allowed us to reduce the
number of parameters from 8 to 4. Among all the
proposed models, the forecasting accuracy for
logistic regression (78.05%) was better than the
accuracy in [1] (LR 75.32% ), [2] (NB - 76.30%),
[3] (NB - 76.3021%), [4] (RF - 77.21%) and [5]
(ANN 75.7%).
6. References
[1] Sisodia, Deepti, and Dilip Singh Sisodia.
"Prediction of diabetes using classification
algorithms." Procedia computer science 132
(2018): 1578-1585.
[2] Alam, Talha Mahboob, et al. "A model for
early prediction of diabetes." Informatics in
Medicine Unlocked 16 (2019): 100204.
[3] Tigga, Neha Prerna, and Shruti Garg.
"Prediction of type 2 diabetes using machine
learning classification methods." Procedia
Computer Science 167 (2020): 706-716.
[4] Diwani, Salim Amour, and Anael Sam.
"Diabetes forecasting using supervised
learning techniques." Adv Comput Sci an Int
J 3 (2014): 10-18.