Improving the Accuracy of Diabetes Diagnosis Applications through a Hybrid Feature Selection Algorithm

Li, Xiaohua; Ju-sheng, Zhang; Safara, Fatemeh

doi:10.1007/s11063-021-10491-0

Cited by 34 publications

(20 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…CNN is a feedforward neural network with convolution calculation, which is the representative algorithms of deep learning [15]. CNN is a multi-layer neural ne Its core parts are the convolutional layer and pooling layer, which can effectively potential information from a large number of samples [18].…”

Section: Cnnmentioning

confidence: 99%

“…CNN is a feedforward neural network with convolution calculation, which is one of the representative algorithms of deep learning [15]. CNN is a multi-layer neural network.…”

Section: Cnnmentioning

confidence: 99%

“…When the original condition attribute set is reduced by 34%, 97% classification accuracy can still be obtained. In the field of diabetes risk prediction, Li X et al [15] combined GA with K-means clustering algorithm for feature selection and then used KNN for classification. Compared with previous related studies, the model performed better.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Early Risk Prediction of Diabetes Based on GA-Stacking

et al. 2022

View full text Add to dashboard Cite

Early risk prediction of diabetes could help doctors and patients to pay attention to the disease and intervene as soon as possible, which can effectively reduce the risk of complications. In this paper, a GA-stacking ensemble learning model is proposed to improve the accuracy of diabetes risk prediction. Firstly, genetic algorithms (GA) based on Decision Tree (DT) is used to select individuals with high adaptability, that is, a subset of attributes suitable for diabetes risk prediction. Secondly, the optimized convolutional neural network (CNN) and support vector machine (SVM) are used as the primary learners of stacking to learn attribute subsets, respectively. Then, the output of CNN and SVM is used as the input of the mate learner, the fully connected layer, for classification. Qingdao desensitization physical examination data from 1 January 2017 to 31 December 2019 is used, which includes body temperature, BMI, waist circumference, and other indicators that may be related to early diabetes. We compared the performance of GA-stacking with K-nearest neighbor (KNN), SVM, logistic regression (LR), Naive Bayes (NB), and CNN before and after adding GA through the average prediction time, accuracy, precision, sensitivity, specificity, and F1-score. Results show that prediction efficiency can be improved by adding GA. GA-stacking has higher prediction accuracy. Moreover, the strong generalization ability and high prediction efficiency of GA-stacking have also been verified on the early-stage diabetes risk prediction dataset published by UCI.

show abstract

Section: Cnnmentioning

confidence: 99%

“…CNN is a feedforward neural network with convolution calculation, which is one of the representative algorithms of deep learning [15]. CNN is a multi-layer neural network.…”

Section: Cnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Early Risk Prediction of Diabetes Based on GA-Stacking

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Zhang et al [13] have used the concept of data augmentation for overcoming the problem of insufficient data.…”

Section: Related Workmentioning

confidence: 99%

Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation

Ahamed

Arya

Nancy

2022

Advances in Human-Computer Interaction

View full text Add to dashboard Cite

The technical improvements in healthcare sector today have given rise to many new inventions in the field of artificial intelligence. Patterns for disease identification are carried out, and the onset of prediction of many diseases is detected. Diseases include diabetes mellitus disease, fatal heart diseases, and symptomatic cancer. There are many algorithms that have played a critical role in the prediction of diseases. This paper proposes an ML based approach for diabetes mellitus disease prediction. For diabetes prediction, many ML algorithms are compared and used in the proposed work, and finally the three ML classifiers providing the highest accuracy are determined: RF, GBM, and LGBM. The accuracy of prediction is obtained using two types of datasets. They are Pima Indians dataset and a curated dataset. The ML classifiers LGBM, GB, and RF are used to build a predictive model, and the accuracy of each classifier is noted and compared. In addition to the generalized prediction mechanism, the data augmentation technique is also used, and the final accuracy of prediction is obtained for the classifiers LGBM, GB, and RF. A comparative study and demonstration between augmentation and non-augmentation are also discussed for the two datasets used in order to further improve the performance accuracy for predicting diabetes disease.

show abstract

“…X. Li et al (2021) developed an automated diabetes diagnosis system for accurate identification of diabetes disease. The proposed system works into three phases‐pre‐processing, feature selection and classification.…”

Section: Related Workmentioning

confidence: 99%

Hybrid diabetes disease prediction framework based on data imputation and outlier detection techniques

Srivastava

Kumar

Singh³

2021

Expert Systems

View full text Add to dashboard Cite

In the field of medical science, accurate prediction is a difficult and challenging task. But, the presence of missing values and outliers can make the prediction task more complicated. Many researchers address the issue of missing value in medical data, either detect the missing value and delete the respective data instances from the dataset or adopt some default methods such as mean, median, neighbour etc., for filling the missing value. However, both methods are lacking to produce optimal results. Furthermore, outliers are also presented in data and degraded the performance of classifier. Few researchers also focus on the outlier detection in medical dataset, but it is not fully explored till date. This work considers the two well‐known problems of data that is, (i) missing value imputation, and (ii) outlier. The missing value imputation issue is addressed through K‐Mean++ based data imputation technique. This technique also validates the data through clustering and also compute the values for missing data. The outlier can be detected through an ABC based outlier detection technique. Further, the final outcome is determined using LS‐SVM classifiers. Hence, this work presents a hybrid disease diagnosis framework for diabetes prediction, called hybrid diabetes prediction framework. The reason behind to choose the diabetes dataset for implementation as it contains 763 missing values and several outliers. The simulation results showed that proposed hybrid framework effectively determines the missing values and outliers in diabetes dataset. Further, the performance of proposed hybrid diabetes prediction framework is evaluated using accuracy, sensitivity, specificity, kappa and AUC parameters and compared with 34 state of art techniques. Results confirmed that proposed hybrid framework obtains 96.57%, 93.37%, 98.12%, 98.17%, and 95.43% accuracy, sensitivity, specificity, kappa and AUC rate respectively.

show abstract

Improving the Accuracy of Diabetes Diagnosis Applications through a Hybrid Feature Selection Algorithm

Cited by 34 publications

References 36 publications

Early Risk Prediction of Diabetes Based on GA-Stacking

Early Risk Prediction of Diabetes Based on GA-Stacking

Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation

Hybrid diabetes disease prediction framework based on data imputation and outlier detection techniques

Contact Info

Product

Resources

About