Ensemble Classification Model for Diabetes Prediction in Data Mining

2019 ◽  
Vol 7 (5) ◽  
pp. 1643-1647
Author(s):  
Munendra Kumar ◽  
Anuj Kumar
Keyword(s):  
Data Mining ◽  
Ensemble Classification ◽  
Classification Model ◽  
Diabetes Prediction

scholarly journals Ensemble Classification Model for Diabetes Prediction in Data Mining

2019 ◽  
Vol 8 (4) ◽  
pp. 1240-1243
Keyword(s):  
Data Mining ◽  
Execution Time ◽  
Ensemble Classification ◽  
Classification Model ◽  
Prediction Analysis ◽  
Current Information ◽  
Proposed Model ◽  
Improve Accuracy ◽  
The Future ◽  
Diabetes Prediction

The prediction analysis is the approach which can predict the future possibilities based on the current information. The diabetes prediction is the approach which is applied to predict the diabetes based on the various attributes. The diabetes dataset has various attributes and based on that attributes diabetes can be predicted. In the previous years approach of SVM is applied for the diabetes prediction. To improve accuracy of diabetes prediction voting based classification is applied in this paper. The proposed model is implemented in python and results are analyzed in terms of accuracy, execution time

Diabetes Prediction using Data Mining

2019 ◽  
Vol 7 (3) ◽  
pp. 749-753
Author(s):  
Suhasini Vijaykumar ◽  
Manjiri Moghe
Keyword(s):  
Data Mining ◽  
Diabetes Prediction ◽  
Using Data

scholarly journals Hybrid classification model to detect advanced intrusions using data mining techniques

2018 ◽  
Vol 7 (2.4) ◽  
pp. 10
Author(s):  
V Mala ◽  
K Meena
Keyword(s):  
Data Mining ◽  
Learning Algorithm ◽  
Detection System ◽  
Classification Model ◽  
Detection Methods ◽  
Data Mining Techniques ◽  
Detection Systems ◽  
Intruder Detection ◽  
Hybrid Classification ◽  
Using Data

Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn about the attack which is fast and reliable to known. In order to detect advanced attacks, unsupervised learning methodologies were employed to detect the presence of zero day/ new attacks. The main objective is to review, different intruder detection methods. To study the role of Data Mining techniques used in intruder detection system. Hybrid –classification model is utilized to detect advanced attacks.

scholarly journals Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongyan Wang
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Test Score ◽  
Nearest Neighbor ◽  
Classification Model ◽  
Data Mining Algorithm ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Classification Efficiency

This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated.

scholarly journals Performance Analysis and Prediction Student Performance to build effective student Using Data Mining Techniques

2019 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Ardalan Husin Awlla
Keyword(s):  
Data Mining ◽  
Performance Analysis ◽  
Student Performance ◽  
Fraud Detection ◽  
Classification Model ◽  
Data Mining Techniques ◽  
Incipient Stage ◽  
The Everyday ◽  
Using Data

In this period of computerization, schooling has additionally remodeled itself and is not restrained to old lecture technique. The everyday quest is on to discover better approaches to make it more successful and productive for students. These days, masses of data are gathered in educational databases, however it stays unutilized. To be able to get required advantages from such major information, effective tools are required. Data mining is a developing capable tool for examination and expectation. It is effectively applied in the field of fraud detection, marketing, promoting, forecast and loan assessment. However, it is in incipient stage in the area of education. In this paper, data mining techniques have been applied to construct a classification model to predict the performance of students.

Markov models to detect the deterioration of patients in a pediatric intensive care unit: analytical observational study Ledys M. Izquierdo MD, MSc, Luis F. Niño PhD Jhon S. Rojas (Preprint)

2021 ◽  
Author(s):  
Ledys Izquierdo
Keyword(s):  
Data Mining ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Pediatric Intensive Care Unit ◽  
Confusion Matrix ◽  
Pediatric Intensive Care ◽  
Warning Signs ◽  
Classification Model ◽  
Military Hospital ◽  
The City

BACKGROUND In the field of continuous vital-sign monitoring in critical care settings, it has been observed that the “early-warning signs” of impending physiological deterioration can fail to be detected timely and sometimes by resource constrained clinical staff. OBJECTIVE to develop a probabilistic model to detect the deterioration of patients in a pediatric intensive care unit. METHODS cross-sectional cohort study, pediatric intensive care unit of the Central Military Hospital in the city of Bogota, Colombia. Children from 1 to 18 years old from January 2018 to January 2020. The CRISP-DM (CRoss-Industry Standard Process for Data Mining) methodology was used as a data mining process and then we used Markov chains to identify the clinical states through which the patient passes. Then, a Hidden Markov model (HMM) based approach is applied for classification and prediction of patient's deterioration by computing the probability of future clinical states. RESULTS Both learning models were trained and evaluated using six vital signs data from 94,678 patient records, collected from the database of real patients who were in the Pediatric Intensive Care Unit of the Central Military Hospital in the city of Bogota, Colombia. To obtain the HMM based classification model, 10-fold cross validation was performed. the confusion matrix showed, Accuracy :0,7, precision: 0.75 and the F1 score:0.65. CONCLUSIONS classification analysis in medical applications can be very useful if considered as a very significant support tool for health professionals. CLINICALTRIAL does not apply

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