Mining of multiple ailments correlated to diabetes mellitus

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

doi:10.1007/s12065-020-00432-6

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…On Stress Detection so many authors worked on various categories by using ML models. From that driver distraction [4], hazard anticipation using real-time in cardiology patients [6], working employees in various organizations [12], performance of the employer after feeling the stress [14], Students assessments on mood disorders [10], there is chance to get diabetes when any person feels stress and they used to get some correlated diabetes ailments [16], predicted the stress on COVID-19 patients [13], after doing the more work without taking the rest, then they feel stress and they may go for depression [30], we are regularly observing the news and reports on social media [31]. Observed that, till now various ML models were used on various datasets but the PCA was not applied to the features.…”

Section: Resultsmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Pradhan,

Shankar,

Barik

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

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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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“…In the event of an emergency, the patient or family members might readily speak with one another and gain a better understanding of the situation. Gao et al, [13] devised a patient monitoring system that used antenna radar for health management. The model is made up of a number of sensors that are linked together via a wireless media.…”

Section: Health Monitoring Systemmentioning

confidence: 99%

Automatic Health Monitoring Patient’s Medical Device

2023

IJBPAS

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Diabetes affects a large number of people all around the world. While contemporary therapies for diabetes, such as insulin injections and insulin pump devices, are successful, they still necessitate a physical effort that, if not addressed quickly enough, can result in death. This project develops and installs an artificial pancreas to help diabetics enhance their quality of life by managing blood glucose levels and easing the burden of insulin therapy. It shows a portable artificial pancreas system that uses a continuous glucose monitor (CGM), an insulin pump, and a CPU to develop a control algorithm for automatic insulin dosage injection. By delivering a regular report to the health care centre and triggering an alarm in the event of any severe issue, the system also serves as an effective tool for monitoring the patient's situation.When compared to commercially available systems, the provided system is more costeffective.

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Mining of multiple ailments correlated to diabetes mellitus

Cited by 12 publications

References 22 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

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

Automatic Health Monitoring Patient’s Medical Device

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