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Faculty of Business Administration, Department of Quantitative Methods,Istanbul University, Turkey

*Corresponding Author:
Gursoy UTS
Faculty of Business Administration
Department of Quantitative Methods
Istanbul University, Turkey
Tel: +90 212 440 00 00
E-mail: [email protected]

Received date: November 25, 2016; Accepted date: November 29, 2016; Published date: November 30, 2016

Citation: Gursoy UTS. Defining Characteristics of Diabetic Patients by Using Data Mining Tools. J Hosp Med Manage. 2016, 2:2.

 
Visit for more related articles at Journal of Hospital & Medical Management

Abstract

Most organizations have large databases that contain wealth of potentially accessible information. Data mining techniques can be used to discover hidden patterns that are unknown a priori. Data mining is the process of selection, exploration and modelling of large quantities of data. Data mining has worthy applications in finance, communication, education, marketing and health management. In this study health management is chosen as an application area. It is very important to encountered similarities of past period cases and definition of patient profile in the health services quickly and to decide correctly. It is aimed to define specific characteristics of diabetic patients in Turkey by using Cluster Analysis and Association Rules

Keywords

Diabetic patients; Association rules; Cluster analysis; Data mining

Introduction

Among chronic diseases, diabetes is increasingly becoming a threat to all age groups on a global scale. Diabetes mellitus prevention and control studies are being conducted commonly. As well as making lifestyle changes, people with diabetes often need additional treatments such as medication like insulin to control their diabetes, blood pressure and blood fats. Diabetes, often referred as diabetes mellitus, describes a group of metabolic diseases in which the person has high blood glucose (blood sugar), either because insulin production is inadequate, or because the body''s also called HbA1c. People who have diabetes need this test regularly to see if their levels are staying within range. It can tell if you need to adjust your diabetes medicines. The A1c test is also used to diagnose diabetes. If your glucose levels have been high over recent weeks, your hemoglobin A1c test will be higher. According to the data, this value is over 5 units in patients (Figure 6).

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Urea

This value should be 5 to 25 mg / dl for a healthy people. It is above the value of 25 mg / dl in participants in the dataset. When this value is exceeded, Type 2 diabetes can lead to kidney failure (Figure 7).

Figure 7: Histogram of Urea.

Creatinine

Creatinine blood test is a biochemical test used to evaluate renal function. In healthy individuals, the creatinine value should be between 0.5 and 1.30 mg / dl. Participants in the study are seen around 1 mg / dL intensively (Figure 8).

Figure 8: Histogram of Creatinine.

Total cholesterol

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HDL cholesterol

It is desirable to be at least 40, for healthy individuals. The distribution of patients is concentrated around this value.

LDL cholesterol

A low value is desirable. The normal value of this measure is between 60-130 mg / dL. A value of 130 or higher is considered abnormal. The variance of the distribution is high in the dataset.

VLDL cholesterol

Very-low-density lipoprotein (VLDL) cholesterol is produced in the liver and released into the bloodstream to supply body tissues with a type of fat (triglycerides). For healthy individuals this value should be between 10-40 mg / dl. There are patients who are quite above the level.

Coronary artery disease

Coronary artery disease (CAD) is the most common type of heart disease. Risk factors include: high blood pressure, smoking, diabetes, lack of exercise, obesity, high blood cholesterol, poor diet, and excessive alcohol, among others. In the dataset 8 people have this disease with a rate of 5.41%.

Cluster Analysis

Cluster analysis is the well-known descriptive data mining method. The objective of cluster analysis is to cluster the observations into groups that are internally homogeneous and heterogeneous from group to group. Choosing the right number of clusters is fundamentally important [2].

K-means method is used for the Cluster analysis. K-means [3] is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster. These centroids shoud be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other. The next step is to take each point belonging to a given data set and associate it to the nearest centroid. When no point is pending, the first step is completed and an early groupage is done. At this point we need to re-calculate k new centroids as barycenters of the clusters resulting from the previous step. After we have these k new centroids, a new binding has to be done between the same data set points and the nearest new centroid. A loop has been generated. As a result of this loop we may notice the 1 last update 02 Jul 2020 that the k centroids change their location step by step until no more changes are done. In other words centroids do not move any more.K-means method is used for the Cluster analysis. K-means [3] is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster. These centroids shoud be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other. The next step is to take each point belonging to a given data set and associate it to the nearest centroid. When no point is pending, the first step is completed and an early groupage is done. At this point we need to re-calculate k new centroids as barycenters of the clusters resulting from the previous step. After we have these k new centroids, a new binding has to be done between the same data set points and the nearest new centroid. A loop has been generated. As a result of this loop we may notice that the k centroids change their location step by step until no more changes are done. In other words centroids do not move any more.

The most appropriate cluster number is 2 for the dataset. Cluster profiles can be seen in Table 1.

Table 1: The Profile of the the 1 last update 02 Jul 2020 ClustersTable 1: The Profile of the Clusters

Association Rules

Association rules are derived from a type of analysis that extracts information from coincidence. This methodology allows to discover correlations, or co-occurences of transactional events. Association rules analysis will be most useful when doing exploratory analyses, looking for interesting relationships that might exist within a dataset [4]. The classic application of association rule mining is the market basket data analysis, which aims to discover how items purchased by customers in a supermarket or a store are associated. Besides market basket the 1 last update 02 Jul 2020 data, association analysis is also applicable to other application domains such as bioinformatics, medical diagnosis, web mining, education, finance and scientific data analysis [5]. In this paper a medical application of association rules are used.Association rules are derived from a type of analysis that extracts information from coincidence. This methodology allows to discover correlations, or co-occurences of transactional events. Association rules analysis will be most useful when doing exploratory analyses, looking for interesting relationships that might exist within a dataset [4]. The classic application of association rule mining is the market basket data analysis, which aims to discover how items purchased by customers in a supermarket or a store are associated. Besides market basket data, association analysis is also applicable to other application domains such as bioinformatics, medical diagnosis, web mining, education, finance and scientific data analysis [5]. In this paper a medical application of association rules are used.

Rule support and confidence are two measures of rule interestingness. They respectively reflect the usefulness and certainty of discovered rules. Typically, associated rules are considered interesting if they satisfy both a minimum support threshold and a minimum confidence threshold. Such thresholds can be set by users or domain experts. Additional analysis can be performed to uncover interesting statistical correlations between associated items [2].

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Figure 9: Web Graph.

Rulesets can be seen in Table 2.

Table 2: Results.

• 80.851% of the patients who have hypertension, also use metformin. The support rate is 63.514%.

• 79.545% of the patients who have hyperlipidemia, also use metformin. The support rate is 59.459%.

• 68% of the patients who have insulin resistance and use metformin, also have hypertension. The support rate is 16.892%.

• 65.909% of the patients who have hyperlipidemia, also have hypertension. The support rate is 59.459%.

Conclusion

Diabetes is a chronic, metabolic disease characterized by elevated levels of blood glucose, which leads over time to serious damage to the heart, blood vessels, eyes, kidneys, and nerves. For people living with diabetes, access to affordable treatment, including insulin, is critical to their survival. There is a globally agreed target to halt the rise in diabetes and obesity by 2025 [6,7]. Diabetes mellitus has increased all over the world in recent years. Because of its importance in this paper the profile of the diabetic patients are tried to be identified by using Cluster analysis. All of the related variables are examined in detail. Association rules show which symtoms occured together. In next studies preventive policies can be studied.

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This work is supported by research fund of Istanbul University (BAP) with the project number of 23408.

References

  1.  https://www.diabetesresearch.org/what-is-diabetes.
  2. Giudici P (2003) Applied Data Mining. Wiley pp:76-77.
  3. https://www.diabetesresearch.org/what-is-diabetes
  4. Mac Queen JB (1967) Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability.Statistics 1: 281-297
  5. Westhpal C, Blaxton T (1998) Data Mining Solutions: Methods and Tools for Solving Real-World Problems. Wiley pp: 186-189
  6. Tan PN, Steinbach M, Kumar V (2006) Introduction to Data Mining. Addison-Wesley pp: 328
  7. Han J, Kamber M (2006) Data Mining Concepts and Techniques. Morgan Kaufmann pp: 229-230
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