scholarly journals Implementation of Predictive Data Mining Techniques for Identifying Risk Factors of Early AVF Failure in Hemodialysis Patients

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Mohammad Rezapour ◽  
Morteza Khavanin Zadeh ◽  
Mohammad Mehdi Sepehri
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Research Society ◽  
Hemodialysis Patients ◽  
Diabetic Patients ◽  
Prediction Methods ◽  
Early Failure ◽  
Data Mining Approach ◽  
Predictive Data Mining ◽  
Accuracy Rates

Arteriovenous fistula (AVF) is an important vascular access for hemodialysis (HD) treatment but has 20–60% rate of early failure. Detecting association between patient's parameters and early AVF failure is important for reducing its prevalence and relevant costs. Also predicting incidence of this complication in new patients is a beneficial controlling procedure. Patient safety and preservation of early AVF failure is the ultimate goal. Our research society is Hasheminejad Kidney Center (HKC) of Tehran, which is one of Iran's largest renal hospitals. We analyzed data of 193 HD patients using supervised techniques of data mining approach. There were 137 male (70.98%) and 56 female (29.02%) patients introduced into this study. The average of age for all the patients was 53.87 ± 17.47 years. Twenty eight patients had smoked and the number of diabetic patients and nondiabetics was 87 and 106, respectively. A significant relationship was found between “diabetes mellitus,” “smoking,” and “hypertension” with early AVF failure in this study. We have found that these mentioned risk factors have important roles in outcome of vascular surgery, versus other parameters such as “age.” Then we predicted this complication in future AVF surgeries and evaluated our designed prediction methods with accuracy rates of 61.66%–75.13%.

scholarly journals Predicting Auditor Changes Using Financial Distress Variables And The Multiple Criteria Linear Programming (MCLP) And Other Data Mining Approaches

2011 ◽  
Vol 27 (5) ◽  
pp. 73 ◽  
Author(s):  
Wikil Kwak ◽  
Susan Eldridge ◽  
Yong Shi ◽  
Gang Kou
Keyword(s):  
Data Mining ◽  
Linear Programming ◽  
Financial Distress ◽  
Multiple Criteria ◽  
Financial Statement ◽  
Font Size ◽  
Explanatory Variables ◽  
Data Mining Approach ◽  
Starting Point ◽  
Accuracy Rates

<span style="font-family: Times New Roman; font-size: small;"> </span><h1 style="margin: 0in 0.5in 0pt; text-align: justify; page-break-after: auto; mso-pagination: none;"><span style="font-family: Times New Roman;"><span style="color: black; font-size: 10pt; mso-themecolor: text1;">Our study evaluates a multiple criteria linear programming (MCLP) </span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;">and other </span><span style="color: black; font-size: 10pt; mso-themecolor: text1;">data mining approach</span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;">es</span><span style="color: black; font-size: 10pt; mso-themecolor: text1;"> </span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;">to predict auditor changes using a portfolio of financial statement measures to capture financial distress</span><span style="color: black; font-size: 10pt; mso-themecolor: text1;">.<span style="mso-spacerun: yes;"> </span>The results of the MCLP approach and the other data mining approaches show that these methods perform</span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;"> reasonably well to predict auditor changes </span><span style="color: black; font-size: 10pt; mso-themecolor: text1;">using financial distress variables.</span><span style="color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-language: KO;"><span style="mso-spacerun: yes;"> </span>Overall accuracy rates are more than 60 percent, and true positive rates exceed 80 percent.<span style="mso-spacerun: yes;"> </span>Our study is designed to establish a starting point for auditor-change prediction using financial distress variables.<span style="mso-spacerun: yes;"> </span>Further research should incorporate additional explanatory variables and a longer study period to improve prediction rates.</span></span></h1><span style="font-family: Times New Roman; font-size: small;"> </span>

scholarly journals A detailed investigation in determining Alzheimer’s disease and its risk factor using different classification techniques

2021 ◽  
Vol 12 (1) ◽  
pp. 374-377
Author(s):  
Mahendran Radha ◽  
Anitha M ◽  
Jeyabaskar Suganya
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Data Mining ◽  
Alzheimer's Disease ◽  
Genetic Disorders ◽  
Learning Approaches ◽  
Psychiatric Illnesses ◽  
Data Mining Approach ◽  
Diseased Person ◽  
Linear Svm

The prevalence of genetic disorders has recently crept surprisingly high. Neurodegenerative complications, specifically, pose physical and mental stress to parents and caretakers. These complications may be witnessed in the case of dementia. The general dementia type that accounted for between 60 to 80 per cent of psychiatric illnesses was Alzheimer's disease. At an earlier stage, illness detection serves as a critical task that helps the diseased person to enjoy a decent quality of life. It has become a much necessitated strategy towards relying on automated techniques like data mining approach for early diagnosis and assessment of risk factors concerned with Alzheimer’s. There has been an unprecedented growth of interest concerned with devising novelized approaches proposed in recent times for classifying the disease. However, there is still a grave need for developing an efficacious approach for better prognosis and classification. Data mining is carried out using different machine-learning approaches to assess the risk factors for Alzheimer's disease. Through the present research, and we compared numerous classification methods such as Decision Tree, Linear SVM, KNN, Logistic Regression, Radial SVM, and Random Forest, and finally reported the most outstanding approach in terms of its accuracy.

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.

scholarly journals 851 Risk Factors Associated with Life Threatening Infections in Children with Febrile Neutropenia a Data Mining Approach

2012 ◽  
Vol 97 (Suppl 2) ◽  
pp. A245-A245
Author(s):  
Z. Badiei ◽  
M. Alami ◽  
M. Khalesi ◽  
H. Farhangi ◽  
A. Banihashem ◽  
...  
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Febrile Neutropenia ◽  
Factors Associated ◽  
Data Mining Approach ◽  
Life Threatening

Optimal Product Portfolio Formulation by Merging Predictive Data Mining With Multilevel Optimization

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
Conrad S. Tucker ◽  
Harrison M. Kim
Keyword(s):  
Data Mining ◽  
Product Family ◽  
Product Architecture ◽  
Product Portfolio ◽  
Market Demand ◽  
Customer Preference ◽  
Data Mining Approach ◽  
Engineering Level ◽  
Initial Stage ◽  
Predictive Data Mining

This paper addresses two important fundamental areas in product family formulation that have recently begun to receive great attention. First is the incorporation of market demand that we address through a data mining approach where realistic customer preference data are translated into performance design targets. Second is product architecture reconfiguration that we model as a dynamic design entity. The dynamic approach to product architecture optimization differs from conventional static approaches in that a product architecture is not fixed at the initial stage of product design, but rather evolves with fluctuations in customer performance preferences. The benefits of direct customer input in product family design will be realized through the cell phone product family example presented in this work. An optimal family of cell phones is created with modularity decisions made analytically at the engineering level that maximize company profit.

scholarly journals Predicting COVID-19 Incidence Using Data Mining Techniques: A case study of Pakistan

2020 ◽  
Vol 11 (4) ◽  
pp. 168-184
Author(s):  
Saba NOOR ◽  
◽  
Waseem AKRAM ◽  
Touseef AHMED ◽  
Qurat-ul-Ain Qurat-ul-Ain ◽  
...  
Keyword(s):  
Data Mining ◽  
Machine Learning Algorithms ◽  
Wuhan City ◽  
Data Mining Approach ◽  
Life Threatening ◽  
Precautionary Measures ◽  
The Government ◽  
Predictive Data Mining ◽  
Using Data

The Outbreak of Coronavirus (COVID-19) came to the world in early December 2019. The early cases of coronavirus were reported in Wuhan City, Hubei Province, China. Till May 18, 2020, 198 countries have been affected by this life-threatening disease. The most common and known traits of COVID-19 are tiredness, fever, and dry cough. In this paper, we have discussed the Predictive data mining approach for COVID-19 predictions. In Predictive data mining, a model is developed and trained using supervised learning and then it predicts the behavior of provided data. Predictive data mining is a renowned technique known to many health organizations for the classification and prediction of diseases such as Heart disease and various types of cancers etc. There are several factors for comparing the model's accuracy, scalability, and interpretability. This predictive model is compared to the basics of its accuracy. In this proposed approach, we have used WEKA as it provides a vast collection of many machine learning algorithms. The main objective of this paper is to forecast the possible future incidence of corona cases in Pakistan. This study concludes that the number of corona cases will increase swiftly. If the government take proactive steps and strictly implement precautionary measures, then Pakistan may be able to overcome this pandemic.

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