Safe Prediction of Diabetes Mellitus Using Weighted Conglomeration of Mining Schemes

Reddy, Shiva Shankar; Sethi, Nilambar; Rajender, R.

doi:10.1109/iceca49313.2020.9297390

Cited by 9 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After the feature selection, the models were applied and compared with the existing models. Hence it was proved that our proposed work had the best accuracy of 94% compared with 92% [7], 91% [10], 92.37% [11], 87.4% [12], 85% [14], 91.5% [15], 90.76% [18], 89.54% [19], 87% [22] and 91% [23]. The proposed work was done with a rare dataset, and the features were selected using PSP with KNN to predict the exact target value so that the patients would not suffer with these correct results.…”

Section: Discussionmentioning

confidence: 69%

“…This approach predicts a dependent variable that falls into categories based on independent factors. LR helps to analyze data and understand the relationship between a dependent binary variable and one or more independent variables [10]. It is a method to determine the connection between a reliant binary variable and one or more autonomous variables.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of cardiovascular disease in diabetic patients using machine learning techniques

Nrusimhadri,

Swain,

Rao

et al. 2024

IJPHS

View full text Add to dashboard Cite

Machine learning (ML) improves operations in many industries, including medicine. It affects the prognosis of several disorders, including heart disease. If predicted, it may provide doctors with new insights and allow them to treat each patient individually. If anticipated, it may provide medical practitioners with valuable information. Our team uses machine learning algorithms to study heart disease risk. This research will compare decision trees, AdaBoost, support vector machines, artificial neural networks (ANN), and customized ANN. The study will include this analysis. The given model will leverage the dataset of general information and medical test results. Our model uses particle swarm optimization (PSO) and k-nearest neighbors (KNN). Algorithm for feature selection. The model reduces dimensionality using evolutionary algorithms and neural networks. We compared the numerous assessment criteria to the current models, our model, and earlier models. Because of this, the suggested model's suitability was rated with the highest accuracy.

show abstract

Section: Discussionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Evaluation of cardiovascular disease in diabetic patients using machine learning techniques

Nrusimhadri,

Swain,

Rao

et al. 2024

IJPHS

View full text Add to dashboard Cite

show abstract

“…They received 95 % accuracy when 10-fold cross-validation was used. S.S. Reddy et al [30] have researched GDM, and also worked on DM prediction in [31], multiple ailments in [32], correlated ailments in [33], predicted readmission patients are admitted or not in [34], conglomerative schemes in [35] and Diagnosis of Diabetes in [36].…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors

Reddy¹,

Gadiraju²,

Nanjundan³

et al. 2023

EAI Endorsed Scal Inf Syst

View full text Add to dashboard Cite

Gestational diabetes mellitus occurs due to high glucose levels in the blood. Pregnant women are affected by this type of diabetes. A blood test is to be performed to identify diabetes. The Oral Glucose Tolerance Test (OGTT) is a blood test performed between the 24th and 28th week of pregnancy that is necessary to identify and overcome the side effects of GDM. The main objective of this work is to train a model by utilizing the training data, evaluate the trained model using the test data, and compare existing machine learning algorithms with a Gradient boosting machine (GBM) to achieve a better model for the effective prediction of gestational diabetes. In this work, the analysis was done with a few existing algorithms and the Extreme learning machine and Gradient boosting techniques. The k-fold cross-validation technique is applied with values of k as 3, 5, and 10 to obtain better performance. The existing algorithms implemented are the Naive Bayes classifier, Support Vector Machine, K-Nearest Neighbour, ID3, CART and J48. The proposed algorithms are Gradient boosting and ELM. These algorithms are implemented in R programming. The metrics like accuracy, kappa statistic, sensitivity/Recall, specificity, precision, f-measure and AUC are used to compare all the algorithms. GBM has obtained better performance than existing algorithms. Then finally, GBM is compared with the other proposed robust Machine Learning algorithm, namely the Extreme learning machine, and the GBM performed better. So, It is recommended to use a gradient-boosting algorithm to predict gestational diabetes effectively.

show abstract

“…By using these models [25], [26] they can predict whether the patient was suffering with the exact problem or not [27], [28]. There is a chance to suffer with ailments of the DM to BC also [29], [30]. A Novel NNa real-time biopsy-based automated system for the diagnosis of BC was used by Singh et al [31].…”

Section: Literature Reviewmentioning

confidence: 99%

A comparative study to predict breast cancer using machine learning techniques

Reddy¹,

Pilli²,

Voosala³

et al. 2022

IJEECS

View full text Add to dashboard Cite

Detection of disease at the starting stage is a very crucial problem. As the population growth increases, the risk of death incurred by breast cancer rises exponentially. Breast cancer is the most common cancer in women, and it is also the most dangerous of all cancers. Deaths because of breast cancer have b een increasing in recent times. Earlier detection of the disease followed by treatment can reduce the risk and increase survival chances. There will be cases where even medical professionals can make mistakes in identifying the disease. This project deals with the detection of Breast cancer using the cell data of the tumor present in the breast. So, with the help of technologies in machine learning and artificial intelligence can substantially improve the diagnosis accuracy. The development of this project is beneficial in medical decision support systems. Several machine learning techniques, namely Adaboost, multi-layer perceptron (MLP) and stacking classifier; were used, and among all the algorithms, the stacking classifier results in the best accuracy. The accuracies 95.6%, 97.1%, and 99.2% respectively.

show abstract

Safe Prediction of Diabetes Mellitus Using Weighted Conglomeration of Mining Schemes

Cited by 9 publications

References 31 publications

Evaluation of cardiovascular disease in diabetic patients using machine learning techniques

Evaluation of cardiovascular disease in diabetic patients using machine learning techniques

A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors

A comparative study to predict breast cancer using machine learning techniques

Contact Info

Product

Resources

About