Predicting the Early Re-admission of Diabetic Patients Using Different Data Mining Techinques

2021 ◽  
Author(s):  
Zainab T. Al-Ars ◽  
Ali Mahdi Aldabbagh
Keyword(s):  
Data Mining ◽  
Diabetic Patients

Data Mining in Diabetes Diagnosis and Detection

2008 ◽  
pp. 1817-1824 ◽  
Author(s):  
Indranil Bose
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Information Age ◽  
Diabetic Patients ◽  
Diabetes Diagnosis ◽  
Inference Rules ◽  
The World ◽  
The Usa ◽  
Detection And Diagnosis ◽  
Mining Tools

Diabetes is a disease worrying hundreds of millions of people around the world. In the USA, the population of diabetic patients is about 15.7 million (Breault et al., 2002). It is reported that the direct and indirect cost of diabetes in the USA is $132 billion (Diabetes Facts, 2004). Since there is no method that is able to eradicate diabetes, doctors are striving for ways to fight this doom. Researchers are trying to link the cause of diabetes with patients’ lifestyles, inheritance information, age, and so forth in order to get to the root of the problem. Due to the prevalence of a large number of responsible factors and the availability of historical data, data mining tools have been used to generate inference rules on the cause and effect of diabetes as well as to help in knowledge discovery in this area. The goal of this chapter is to explain the different steps involved in mining diabetes data and to show, using case studies, how data mining has been carried out for detection and diagnosis of diabetes in Hong Kong, USA, Poland, and Singapore.

Data Mining in Diabetes Diagnosis and Detection

2011 ◽  
pp. 257-261
Author(s):  
Indranil Bose
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Information Age ◽  
Diabetic Patients ◽  
Diabetes Diagnosis ◽  
Inference Rules ◽  
The World ◽  
The Usa ◽  
Detection And Diagnosis ◽  
Mining Tools

Diabetes is a disease worrying hundreds of millions of people around the world. In the USA, the population of diabetic patients is about 15.7 million (Breault et al., 2002). It is reported that the direct and indirect cost of diabetes in the USA is $132 billion (Diabetes Facts, 2004). Since there is no method that is able to eradicate diabetes, doctors are striving for ways to fight this doom. Researchers are trying to link the cause of diabetes with patients’ lifestyles, inheritance information, age, and so forth in order to get to the root of the problem. Due to the prevalence of a large number of responsible factors and the availability of historical data, data mining tools have been used to generate inference rules on the cause and effect of diabetes as well as to help in knowledge discovery in this area. The goal of this chapter is to explain the different steps involved in mining diabetes data and to show, using case studies, how data mining has been carried out for detection and diagnosis of diabetes in Hong Kong, USA, Poland, and Singapore.

PREDICTING RETINOPATHY RISK AMONG DIABETIC PATIENTS: A DATA MINING APPROACH

2019 ◽  
Vol 31 (02) ◽  
pp. 1950015
Author(s):  
Saghar Foshati ◽  
Malihe Sabeti ◽  
Ali Zamani
Keyword(s):  
Data Mining ◽  
Treatment Method ◽  
Diagnosis And Treatment ◽  
Diabetic Patients ◽  
Related Data ◽  
Data Mining Approach ◽  
Treatment Type ◽  
Treatment Of Diabetes ◽  
Efficacy Criteria ◽  
Shed Light

In medicine, data collection plays an important role in the diagnosis of diseases and treatment of patients. Physicians have to cope with a large amount of patient-related data, and they often have to review the patient’s whole history or other similar cases. Data is mainly collected to find out if there are related patterns and results that can shed light on the nature of the investigated disease. Mechanized data mining has tremendous value in the diagnosis and treatment of diseases, and can be especially helpful in the diagnosis and treatment of diabetes, a disease that inflicts a large portion of the population. Now, diabetes is the fourth cause of mortality among the general population in developing countries. Retinopathy is a chronic complication of diabetes that has serious consequences including blindness if not diagnosed as early as possible. This study uses a sample of 310 Diabetic patients, half of them have the diagnosis of retinopathy ([Formula: see text]) and investigates 29 variables including age, gender, HbA1c, treatment type and etc. Our results indicate that Decorate algorithm in Weka software is the most vigorous algorithm for the purpose of the study with an accuracy rate of 0.86. The study also investigates efficacy criteria related to databases and risk factors related to this disease including age, duration of the disease, BMI, HDL level, HbA1c, FBS, 2HPPG, blood pressure, and treatment method.

Use of Orange Data Mining Toolbox for Data Analysis in Clinical Decision Making

2018 ◽  
pp. 143-167
Author(s):  
Musa Peker ◽  
Osman Özkaraca ◽  
Ali Şaşar
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Blood Sugar ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Diabetic Patients ◽  
Proposed Model ◽  
Treatment Of Diabetes

Diabetes is a life-long illness which occurs as a result of lack of insulin hormone or ineffectiveness of insulin hormone. Blood sugar, fructosamine, and hemoglobin A1c (HbA1c) values are widely used for diagnosis of this disease. Although the role of insulin in diagnosing diabetes is great, the HbA1c value is more accurate. This is because HbA1c value gives information about the past two or three months of blood sugar in the treatment of diabetes. This study aims to estimate the HbA1c value with high accuracy. Follow-up data of diabetic patients were used as data. The Orange data mining software is used because it is easy to use in the modeling phase and contains many methods. In this context, the chapter aims to develop an effective prediction model by using a large number of feature selection and classification methods. The results show that the proposed model successfully predicts the HbA1c parameter. In addition, determination of the parameters that are effective in the diagnosis of diabetes has been carried out with the feature selection methods.

MAPPING WITH THE HELP OF NEW PROPOSED ALGORITHM AND MODIFIED CLUSTER FORMATION ALGORITHM TO RECOMMEND AN ICE CREAM TO THE DIABETIC PATIENT BASED ON SUGAR CONTAIN IN IT

2015 ◽  
Vol 3 (6) ◽  
pp. 410 ◽  
Author(s):  
Suhas Machhindra Gaikwad ◽  
Preeti Mulay ◽  
Rahul Raghvendra Joshi
Keyword(s):  
Data Mining ◽  
Diabetic Patient ◽  
Cluster Formation ◽  
Ice Cream ◽  
Diabetic Patients

The research for suggesting an ice cream for a diabetic patient is carried out in data mining by using clustering and mapping between the data for ice cream and diabetic patients. Here, mapping of ice cream dataset with diabetic patient dataset is done by using MFCA, which is proposed and explained in this paper. The results obtained from MCFA algorithm and the new proposed algorithm are explained and verified and it is observed that they are having the relevance.

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