Type 2 diabetes mellitus prediction model based on data mining

Wu, Han; Yang, Shengqi; Huang, Zhilong; Jian, He; Wang, Xiaoyi

doi:10.1016/j.imu.2017.12.006

Cited by 324 publications

(198 citation statements)

References 21 publications

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“…These weights are specific to each individual variable to neuron. The mapping of hidden layer and output can be given as: (2) Similarly, the output layer's mapping can be given as: (3) In our work, we have considered multiple neurons hence for neurons, the biases are given as . Before applying any activation function, the value of neuron is denoted as where denotes the attributes.…”

Section: Fig1 Neural Network Architecturementioning

confidence: 99%

“…The International Diabetes Federation reported that currently more than 382 million people are affected with diabetes in the worldwide and it is expected that this mark will increase up to 592 million diabetes patients by 2035.The diabetes mellitus is categorized into 2 types as Type-1 and Type-2 diabetes [2]. Type-1 diabetes affects children where patient's body is not capable of using insulin properly.…”

Section: Introductionmentioning

confidence: 99%

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ENN Ensemble based Neural Network method for Diabetes Classification

Kumari,

2020

IJEAT

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Diabetes is considered as one of the most chronic disease which has serious impact on human health and leading cause of mortality worldwide. The early prediction of diabetes can help clinicians to provide a better diagnosis to the patients. Recently, computed aided diagnosis systems have gained attention due to significant growth in data mining, and machine learning. Several approaches are present based on the machine learning techniques but due to poor classification performance and computational complexity, it becomes difficult to utilize for real-time applications. Ensemble classification approaches have reported a noteworthy improvement in diabetes classification but desired accuracy is still a challenging task. Hence, in this work we introduce a combined hybrid approach called as ENN-Ensemble based neural network approach for diabetes classification. In this approach, a feature selection process is presented using neighboring search technique; the selected features are processed through the feature ranking model to generate the efficient feature subset for better classification accuracy. Finally, these features are learned and classified using neural network classifier. The experimental study shows that the proposed approach achieves better accuracy when compared with the existing techniques.

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Section: Fig1 Neural Network Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ENN Ensemble based Neural Network method for Diabetes Classification

Kumari,

2020

IJEAT

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show abstract

“…The paper by Wu H, Yang S, Huang Z, He J, Wang X. [14] has addressed the problem of improving the accuracy of the prediction model, and to make the model adaptive to more than one dataset.…”

Section: Related Workmentioning

confidence: 99%

A short review of machine learning methods for classifying the outcome of Gestational Diabetes

Banerji

2019

Preprint

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Diabetes mellitus is a growing problem, especially in developing countries. People suffering from diabetes have an increased risk of developing a number of serious health problems. Consistently high blood glucose levels can lead to serious diseases affecting the heart and blood vessels, eyes, kidney, etc. In addition, people with diabetes also have a higher risk of developing infections. This paper aims to use suitable data mining and classification techniques which include the Logit model, the Probit model, the Classification tree technique, Artificial Neural Networks, Support Vector Machines, Ridge Regression technique and the Least Absolute Shrinkage and Selection Operator(LASSO) in order to determine the best method which can be used to classify the patients as suffering from gestational diabetes or not. The misclassification rate is calculated for different methods and the method having the least misclassification rate is said to be the most suitable to be applied to the given data, which is the PIMA Indians diabetes dataset.

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“…The main objective of this paper is to improve the accuracy of the prediction model and to more than one dataset model is made adaptive in nature. Proposed model comprised of two parts based on a series of pre-processing procedures [14]. These two parts are improved K-means algorithm and the logistic regression algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Diabetes by adopting Classification Approach in Data Mining

Kaur

2019

JOIV : Int. J. Inform. Visualization

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As the world is growing fast, the metamorphosing of things, lifestyle, perceptions of people and resources is taking place. But the elevation in technology has become a challenge now as the ideas, innovations are amplifying. One of the biggest things the advancement and elevations in technology has given birth is "Big Data". In this data massive amount of information is hidden. In order to refine or process this data and to find out and unmask the insights, many techniques and algorithms have been evolved, one of which is the data mining. The data mining is the approach or procedure which helps in detaching or extracting profitable and fruitful knowledge, reports and facts from the rough or impure data. The prediction analysis is approach comprehended from data mining to forecast and figure out the future making using classification technique. This research work is based on the diabetes prediction by making use of classification approach. In the existing approach SVM classifier is applied for the prediction analysis. To increase accuracy approach of KNN classifier is applied for the prediction analysis. Both the proposed and existing methods are implemented in Python. The simulation results show that accuracy of KNN is increased and execution time is reduced.

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Type 2 diabetes mellitus prediction model based on data mining

Cited by 324 publications

References 21 publications

ENN Ensemble based Neural Network method for Diabetes Classification

ENN Ensemble based Neural Network method for Diabetes Classification

A short review of machine learning methods for classifying the outcome of Gestational Diabetes

Predicting Diabetes by adopting Classification Approach in Data Mining

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