A prediction model for type 2 diabetes using adaptive neuro-fuzzy interface system

Alby, Sophie; Shivakumar, B. L.

doi:10.4066/biomedicalresearch.29-17-254

Cited by 13 publications

(9 citation statements)

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“…The section explores the various literary work which has been already carried out in the same area. In [6] Neuro-fuzzy based interface model is used to predict the diabetes disease. The Pima diabetes data set is accumulated from the National Institute of Diabetes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The MLP with one hidden layer is a mapping between the input vector and output vector shown in the equation (5) below. Hence the matrix representation of the same can be denoted as (6) In the equation (6) above and are the activation function, and are the bias vectors, and are weight matrices. In the proposed approach the sigmoid is used as an activation function in order to achieve the non-linearity.…”

Section: Fig 2proposed Symmetry Based Feature Selection Algorithmmentioning

confidence: 99%

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Symmetry Based Feature Selection with Multi layer Perceptron for the prediction of Chronic Disease

Hegde¹,

Hegde²

2019

IJRTE

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Huge amount of Healthcare data are produced every day from the various health care sectors. The accumulated data can be effectively analyzed to identify people's risk from chronic diseases. The process of predicting the presence or absence of the disease and also to diagnosing the various disease using the historical medical data is known as Health Care Analytics. Health care analytics will improve patient care and also the harness practice of medical practitioner. The feature selection is considered as a core aspect of the machine learning which hugely contribute towards the performance of the machine learning model. In this paper symmetry based feature subset selection is proposed to select the optimal features from the Health care data which contribute towards the prediction outcome. The Multilayer perceptron algorithm(MLP) used as a classifier which will predict the outcome by using the features which are selected from the Symmetry-based feature subset selection technique. The chronic disease dataset Diabetes, Cancer, Breast Cancer, and HeartDisease data set accumulated from UCI repository is used to conduct the experiment. The experimental results demonstrate that the proposed hybrid combination of feature selection technique and the multilayer perceptron outperforms in accuracy compare to the existing approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Fig 2proposed Symmetry Based Feature Selection Algorithmmentioning

confidence: 99%

Symmetry Based Feature Selection with Multi layer Perceptron for the prediction of Chronic Disease

Hegde¹,

Hegde²

2019

IJRTE

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“…The experiment showed that by using five-fold cross-validation, their proposed model produced promising results where it recorded an accuracy rate of 98.35%. One more prediction model for type 2-diabetes has been presented by Alby et al [20]. They have developed an adaptive neuro-fuzzy interface system integrated with genetic algorithms and recorded a 96.08% accuracy rate.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Deep Learning Model to Detect Diabetes Mellitus Based on DNA Sequence

El-Attar¹,

Moustafa²,

Awad³

2022

Intelligent Automation &Amp; Soft Computing

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DNA sequence classification is considered a significant challenge for biological researchers to scientifically analyze the enormous volumes of biological data and discover different biological features. In genomic research, classifying DNA sequences may help learn and discover the new functions of a protein.Insulin is an example of a protein that the human body produces to regulate glucose levels. Any mutations in the insulin gene sequence would result in diabetes mellitus. Diabetes is one of the widely spread chronic diseases, leading to severe effects in the longer term if diagnosis and treatment are not appropriately taken. In this research, the authors propose a hybrid deep learning model based on Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) to classify the DNA sequence for the insulin gene to predict type 2-diabetes based on gene sequence mutation. To evaluate the proposed models, we used several performance indexes such as accuracy, precision, sensitivity, recall, and F1 score. The experiments shown in the paper reveal that the proposed model accomplished the best results. The overall accuracy of the learning process is recorded as 99% for the proposed hybrid LSTM-CNN model while it is recorded as 97.5%, and 95% for CNN, and LSTM, respectively.

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“…Author Alby in his paper has developed ANFIS with GA and General Regression Neural Network (GRNN) for prediction of Type-II DM [4]. Using ANFIS with GA accuracy was 93.49% and accuracy was 85.49% with GRNN classifier.…”

Section: Related Workmentioning

confidence: 99%

Hybrid ANFIS-GA and ANFIS-PSO Based Models for Prediction of Type 2 Diabetes Mellitus

Patil¹,

Tamane

Rawandale

2020

Computational Methods and Data Engineering

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Type-Diabetes Mellitus (T2DM), a major threat to developing as well as developed countries, can be easily controlled to a large extent through lifestyle modifications. Diabetes increases the risk of developing various health as well as financial problems to cure these health complications. The health complications are stroke, myocardial infarction, and coronary artery disease. Nerve, muscle, kidney and retinal damage have distressing impact on the life of a diabetic patient. It is the need of the hour to halt the epidemic of T2DM in the early stage. Data science approaches have the potential to predict on medical data. Machine learning is an evolving scientific field in data science where machines learn mechanically and improve from experience without any explicit program. Our goal was to develop a system which can improve performance of a classifier for prediction of T2DM. The purpose of this work is to implement a hybrid model for prediction by integrating the advantages of artificial neural net (ANN) and fuzzy logic. Genetic algorithm (GA) and particle swarm optimization (PSO) have been applied to optimize parameters of developed predicting model. The proposed scheme used a fuzzification matrix. This matrix is used to relate the input patterns with a degree of membership to different classes. The specific class is predicted based on the value of degree of membership of a pattern. We have analyzed the proposed method and previous research in the literature. High accuracy was achieved using the ANFIS-PSO approach.

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A prediction model for type 2 diabetes using adaptive neuro-fuzzy interface system

Cited by 13 publications

References 0 publications

Symmetry Based Feature Selection with Multi layer Perceptron for the prediction of Chronic Disease

Symmetry Based Feature Selection with Multi layer Perceptron for the prediction of Chronic Disease

Deep Learning Model to Detect Diabetes Mellitus Based on DNA Sequence

Hybrid ANFIS-GA and ANFIS-PSO Based Models for Prediction of Type 2 Diabetes Mellitus

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