Early prediction of diabetes by applying data mining techniques

2019 ◽  
Vol 171 ◽  
pp. 3
Author(s):  
Mohammed Zeyad Al Yousef ◽  
Mazen Ferwana ◽  
Sherif Sakr ◽  
Riyad Al Shammari
Keyword(s):  
Data Mining ◽  
Early Prediction ◽  
Data Mining Techniques ◽  
Prediction Of Diabetes

Early Prediction of Diabetes Disease Based on Data Mining Techniques

2021 ◽  
pp. 40-51
Author(s):  
Salma N. Elsadek ◽  
Lama S. Alshehri ◽  
Rawan A. Alqhatani ◽  
Zainah A. Algarni ◽  
Linda O. Elbadry ◽  
...  
Keyword(s):  
Data Mining ◽  
Early Prediction ◽  
Data Mining Techniques ◽  
Prediction Of Diabetes

Data Mining Techniques for Diagnosis of Diabetes: A Review

2018 ◽  
Vol 6 (9) ◽  
pp. 212
Author(s):  
Tushar Deshmukh ◽  
H. S. Fadewar
Keyword(s):  
Data Mining ◽  
Blood Glucose ◽  
The Body ◽  
Data Mining Techniques ◽  
Early Stages ◽  
Historic Data ◽  
Prediction Of Diabetes

This Diabetes is such a common dieses found all over the globe, in which blood glucose or in normal terminology the sugar level in blood is increased. It is the condition of the body in which the insulin which is required for the metabolism of the food is not created or body cannot use the insulin produced properly. Doctors say that diabetes can be controlled if it is detected in its early stages. Data mining is the process in which the data can be used for the prediction based on historic data. The intention here is to analysis how various researchers have used the data mining for better prediction of diabetes so that it could be controlled and possible even cured.

scholarly journals A Study in the Early Prediction of ICT Literacy Ratings Using Sustainability in Data Mining Techniques

2021 ◽  
Vol 13 (4) ◽  
pp. 2141
Author(s):  
Kyungyeul Kim ◽  
Han-Sung Kim ◽  
Jaekwoun Shim ◽  
Ji Su Park
Keyword(s):  
Data Mining ◽  
Learning Process ◽  
Technology Literacy ◽  
Early Prediction ◽  
Ict Literacy ◽  
Educational Strategies ◽  
Achievement Level ◽  
Data Mining Techniques ◽  
Educational Backgrounds ◽  
Customized Learning

It would be very beneficial to determine in advance whether a student is likely to succeed or fail within a particular learning area, and it is hypothesized that this can be accomplished by examining student patterns based on the data generated before the learning process begins. Therefore, this article examines the sustainability of data-mining techniques used to predict learning outcomes. Data regarding students’ educational backgrounds and learning processes are analyzed by examining their learning patterns. When such achievement-level patterns are identified, teachers can provide the students with proactive feedback and guidance to help prevent failure. As a practical application, this study investigates students’ perceptions of computer and internet use and predicts their levels of information and communication technology literacy in advance via sustainability-in-data-mining techniques. The technique employed herein applies OneR, J48, bagging, random forest, multilayer perceptron, and sequential minimal optimization (SMO) algorithms. The highest early prediction result of approximately 69% accuracy was yielded for the SMO algorithm when using 47 attributes. Overall, via data-mining techniques, these results will aid the identification of students facing risks early on during the learning process, as well as the creation of customized learning and educational strategies for each of these students.

Early Prediction of Chronic Kidney Disease Using Data Mining Techniques

2021 ◽  
pp. 947-957
Author(s):  
Hasin Shahed Shad ◽  
Zeeshan Jamal ◽  
S. M. Foysal Ahmed ◽  
Sifat Momen ◽  
Nafees Mansoor
Keyword(s):  
Data Mining ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Early Prediction ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals Utilization of Machine Learning in Brain Tumor Classification

2019 ◽  
pp. 325-330
Author(s):  
Sanjeet Pandey ◽  
Brijesh Bharadwaj ◽  
Himanshu Pandey ◽  
Vineet Kr. Singh
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Early Stage ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
Diabetes Prediction ◽  
System Data ◽  
Health System Data ◽  
Prediction Of Diabetes

Since past few years data mining lot of attention related to knowledge like extracting methods in health care system like diabetes, cancer, CVS etc. There are lot of technique of data mining like decision tree, Naive base, KNN; J48 etc. are being used for prediction of diabetes. Diabetes is metabolic disorder related to poor absorption of insulin into body mussels or poor lowered secretion of insulin from pancreases. As this disease, this is main death causes disease in the world. So, prediction of these diseases with the help of data mining technique may help to protect many lives. In this study, we have to discuss various data mining technique, types of diabetes, application of these data mining technique. Prediction of diabetes or any other disease could play a significant role in health system. Data mining are very useful in the scenario. These techniques help in selection, understanding and designing of large size data to analysis the chances of diseases occurrence. Recently who has announced diseases a major cause of death worldwide. The prediction and identification early stage of diabetes can play major role to treat this disease significantly. Various data mining techniques like KNN, Decision tree, Naïve Bays etc. would be a significant asset for the researcher for gaining various data about diabetes, its causes, symptoms and possible treatment that have been using in the past and currently used by various physician. In this study we have briefly discussed various data mining techniques/models. Which have been currently used for diabetes prediction? Along with this discussion, we have also focused on performance and short coming of existing models/techniques time to time evaluated by researchers.

Prediction of Diabetes Using Data Mining Techniques

2017 ◽  
Vol 10 (4) ◽  
pp. 1098 ◽  
Author(s):  
V. Mareeswari ◽  
R Saranya ◽  
R Mahalakshmi ◽  
E Preethi
Keyword(s):  
Data Mining ◽  
Data Mining Techniques ◽  
Using Data ◽  
Prediction Of Diabetes
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