A Review of Data Mining Schemes for Prediction of Diabetes Mellitus and Correlated Ailments

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

doi:10.1109/iccubea47591.2019.9128880

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…While kNN with principal component analysis (PCA) had the maximum sensitivity, it had a low specificity, suggesting that the classifier was less accurate than kNN. S.S. Reddy et al [16] have worked on diabetes mellitus (DM) patients and their correlated ailments [17] like renal fault [18]. Through this research, they have identified that people's stress may affect DM.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of stress based on multiple distinct modalities using machine learning techniques

Pradhan,

Shankar,

Barik

et al. 2024

IJPHS

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Nowadays, one of the most time-consuming and complex study subjects is predicting working professionals' stress levels. It is thus crucial to estimate active professionals' stress levels to aid their professional development. Several machine learning (ML) and deep learning (DL) methods have been created in earlier articles for this goal. But they also have drawbacks, such as increased design complexity, a high rate of misclassification, a high incidence of mistakes, and reduced efficiency. Considering these issues, the objective of this study is to make a prognosis about the stress levels experienced by working professionals by using a cutting-edge deep learning model known as the convolutional neural networks (CNN). In this paper, we offer a model that combines CNN-based classification with dataset preprocessing, feature extraction, and optimum feature selection using principal component analysis (PCA). When the raw data is preprocessed, duplicate characteristics are eliminated, and missing values are filled.

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Section: Related Workmentioning

confidence: 99%

Evaluation of stress based on multiple distinct modalities using machine learning techniques

Pradhan,

Shankar,

Barik

et al. 2024

IJPHS

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“…Reddy et al [21] did their work on various diseases like Diabetes [22] and whether its correlated ailments are affected or not. The patient, once discharged from the hospital, are readmitted with multiple causes [23], and they predict whether the patients are diagnosed clearly or not [24] by using ML and DL models [25]. The NELM particle swarm optimization (PSO) algorithm indicated whether the DM patient was affected by BC [26].…”

Section: Related Workmentioning

confidence: 99%

An extensible framework for recurrent breast cancer prognosis using deep learning techniques

Reddy¹,

Swaroop²,

Voosala³

et al. 2023

IJEECS

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<span lang="EN-US">Due to population growth, early illness detection is getting more challenging. Breast cancer is the second-deadliest malignancy. An estimated one million people are newly diagnosed with the disease annually in India. Most cases are never diagnosed because they are either ignored or not reported. Also, secondary malignancies may develop after a breast cancer recurrence, including those of the brain, lungs, and bones. Early detection and treatment of people with recurring breast cancer may help prevent secondary cancers and other disorders. By examining cell and tumour data as well as data from other diseases, this project hopes to overcome this obstacle and more accurately diagnose breast cancer. Accurate diagnosis of breast cancer may be achieved with the use of machine learning techniques. The effort focuses on recurring breast cancer and aims to efficiently identify it. In ensemble learning, decision trees filter out non-essential qualities. Cancer recurrences and non-recurrences are distinguished using voting classifiers. The soft voting classifier classifies a variety of data sets with 98.24% accuracy. The proposed model has an accuracy of 0.97, a recall of 0.97, an F1-Score of 0.969, and a Choen kappa score of 0.9655, as stated by the recommended model.</span>

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“…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

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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.

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A Review of Data Mining Schemes for Prediction of Diabetes Mellitus and Correlated Ailments

Cited by 11 publications

References 23 publications

Evaluation of stress based on multiple distinct modalities using machine learning techniques

Evaluation of stress based on multiple distinct modalities using machine learning techniques

An extensible framework for recurrent breast cancer prognosis using deep learning techniques

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

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