A Machine Learning Based Approach for the Identification of Insulin Resistance with Non-Invasive Parameters using Homa-IR

Chakradar, Madam

doi:10.30534/ijeter/2020/95852020

Cited by 2 publications

(2 citation statements)

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“…The paper [11] Paper [13] identified the insulin resistance using non invasive approaches of machine learning techniques. Experimented the work with CALERIE data set with 18 parameters such as age, gender,height etc., The selected attributes of feature selection is given as input to the classification algorithms such as logistic regression, CART, SVM,LDA,KNN etc., the analysis results shows high accuracy of 97% to identify the insulin resistance while using logistic regression and SVM.…”

Section: Literature Reviewmentioning

confidence: 99%

Recursive Feature Elimination with Ridge Regression (L2) Machine Learning Hybrid Feature Selection Algorithm for Diabetic Prediction using Random Forest Classifer.

Venkatachalam

Prabhu

Abouhawwash

et al. 2021

Preprint

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In day today life, diabetes illness is increasing in count due to the body not able to metabolize the glucose level. The prediction of the right diabetes patients is an important research area that many researchers are proposing the techniques to predict this disease through data mining and machine learning methods. In prediction, feature selection is one of the key concept in preprocessing so that the features that are relevant to the disease will be used for prediction. This will improve the prediction accuracy. Selecting right features among the whole feature set is a complicated process and many researchers are concentrating on it to produce the predictive model with high accuracy. In this proposed work, the wrapper based feature selection method called Recursive Feature Elimination (RFE) is combined with Ridge regression (L2) to form a hybrid L2 regulated feature selection algorithm to overcome the overfilling problem of the data set. Over fitting is the major problem in feature selection which means that the new data are not fit to the model since the training data is small. Ridge regression is mainly used to overcome the overfitting problem. Once the features are selected using the proposed feature selection method, random forest classifier is used to classify the data based on the selected features. The proposed work is experimented in PIDD data set and the evaluated results are compared with the existing algorithms to prove the accuracy effect of the proposed algorithm. From the results obtained by proposed algorithm, the accuracy of predicting the diabetes disease is high compared to other existing algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Recursive Feature Elimination with Ridge Regression (L2) Machine Learning Hybrid Feature Selection Algorithm for Diabetic Prediction using Random Forest Classifer.

Venkatachalam

Prabhu

Abouhawwash

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In any case, while Sacking is about ceaselessly more exact than a solitary classifier, it is now and again substantially less definite than Boosting. The paper [12] is a Machine Learning-Based Approach for the Identification of Insulin Resistance with Non-Invasive Parameters using Homa-IR.…”

Section: Related Workmentioning

confidence: 99%

Diabetes and Heart Disease Prediction Using Machine Learning Algorithms

Pasha¹

2020

IJETER

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Diabetes and Heart Disease are diseases with an ongoing illness that generates an augmentation and variation in the human body. Various troubles occur in case diabetes stays crude and unidentified. The dull perceiving handle occurred in going to of comprehension to a decisive focus and guiding expert. In any case, the ascent in Machine Learning approaches handles this essential issue. The reason for this is to predict the presence of diabetes as well as heart disease in patients with the most outrageous exactness. In this manner, ML counts to be explicit ANN, ELM, PCA, LASSO, Ensemble learning, and SVM was applied to recognize these diseases before it is orchestrated. The accuracy of the above mentioned ML algorithms is evaluated on Diabetes and Heart Disease Datasets.

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A Machine Learning Based Approach for the Identification of Insulin Resistance with Non-Invasive Parameters using Homa-IR

Cited by 2 publications

References 0 publications

Recursive Feature Elimination with Ridge Regression (L2) Machine Learning Hybrid Feature Selection Algorithm for Diabetic Prediction using Random Forest Classifer.

Recursive Feature Elimination with Ridge Regression (L2) Machine Learning Hybrid Feature Selection Algorithm for Diabetic Prediction using Random Forest Classifer.

Diabetes and Heart Disease Prediction Using Machine Learning Algorithms

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