Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

Sun, Yunlei; Zhang, Dalin

doi:10.17559/tv-20190421122826

Cited by 16 publications

(5 citation statements)

References 53 publications

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“…The single-sample gene enrichment analysis method enables the emergence of functionally relevant genomes based on gene discovery profile data analysis. According to the level of gene discovery, the algorithm first sorts all genetic factors to obtain the gene order [ 24 ]. After that, the concentration index of a certain gene is obtained by calculation.…”

Section: Methods Of Immune Infiltration Of Malignant Melanomamentioning

confidence: 99%

[Retracted] Considering Computational Mathematics IGHG3 as Malignant Melanoma Is Associated with Immune Infiltration of Malignant Melanoma

Cao

2022

BioMed Research International

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Malignant melanoma is one of the most threatening cancers to human health. Only 14% of patients with malignant melanoma have a remaining life span of 5 years. At present, there have been some studies looking for potential prognostic indicators of esophageal cancer from the level of genes and infiltrating immune cells, but there are still some problems that need to be resolved urgently. This paper proposes IGHG3 as the immune infiltration of malignant melanoma, which takes into account the computational mathematics. It aims to deduce the characteristics of immune cell infiltration in malignant melanoma and study the relationship between different immune cell infiltration characteristics and prognosis. The method in this article is to establish a computational mathematical model for the immunotherapy of melanoma, then study the method of identification of the affinity of the IGHG3 reagent, and finally obtain the gene expression of immune infiltration. The functions of these methods are, respectively, to predict the dynamic behavior of T cells with two different specificities through mathematical models and to test the matching degree of different concentrations of IGHG3 reagent with the human body. Then use the ssGSEA algorithm to obtain immune infiltration related data and calculate the difference between the weighted empirical cumulative distribution function of all genes in the effect of IGHG3 on melanoma that was carried out. The experimental results showed the computational mathematical method genome and all the remaining genes. In this study, a computational mathematical method to detect the IGHG3 gene expression had a significant inhibitory effect on A375 cells in the experimental group, and the knockdown efficiency reached 85.6%.

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Section: Methods Of Immune Infiltration Of Malignant Melanomamentioning

confidence: 99%

[Retracted] Considering Computational Mathematics IGHG3 as Malignant Melanoma Is Associated with Immune Infiltration of Malignant Melanoma

Cao

2022

BioMed Research International

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“…Diagnosis of diabetes is a growing area of study. Sun and Zhang [ 1 ] have discussed a few deep learning methods and classification methods such as artificial neural network, decision trees, random forest, and support vector machine. Qawqzeh et al [ 4 ] have implemented a logistic regression classification technique for the classification of diabetes data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Diabetes is of three types. First is juvenile diabetes [ 1 ], which occurs mostly in children and destroys the cells which produce insulin in the pancreas. Second is type 2 diabetes, which generally happens after the age of 40 because of lack of exercise and unhealthy lifestyle.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Various Diabetic Prediction Models: A Literature Survey

Saxena

Sharma

Gupta

et al. 2022

Journal of Healthcare Engineering

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Diabetes is a chronic disease characterized by a high amount of glucose in the blood and can cause too many complications also in the body, such as internal organ failure, retinopathy, and neuropathy. According to the predictions made by WHO, the figure may reach approximately 642 million by 2040, which means one in a ten may suffer from diabetes due to unhealthy lifestyle and lack of exercise. Many authors in the past have researched extensively on diabetes prediction through machine learning algorithms. The idea that had motivated us to present a review of various diabetic prediction models is to address the diabetic prediction problem by identifying, critically evaluating, and integrating the findings of all relevant, high-quality individual studies. In this paper, we have analysed the work done by various authors for diabetes prediction methods. Our analysis on diabetic prediction models was to find out the methods so as to select the best quality researches and to synthesize the different researches. Analysis of diabetes data disease is quite challenging because most of the data in the medical field are nonlinear, nonnormal, correlation structured, and complex in nature. Machine learning-based algorithms have been ruled out in the field of healthcare and medical imaging. Diabetes mellitus prediction at an early stage requires a different approach from other approaches. Machine learning-based system risk stratification can be used to categorize the patients into diabetic and controls. We strongly recommend our study because it comprises articles from various sources that will help other researchers on various diabetic prediction models.

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“…Due to rising living standards, diabetes has become more prevalent in people’s everyday lives. Diabetes, commonly referred to as diabetes mellitus, is a chronic condition brought on by a rise in blood glucose levels [ 1 , 2 ]. Numerous physical and chemical tests can be used to detect this condition.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study

Laila

Mahboob

Khan

et al. 2022

Sensors

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Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable healthcare advisors to take care of the conditions. Most diabetic patients know little about the risk factors they face before diagnosis. Nowadays, hospitals deploy basic information systems, which generate vast amounts of data that cannot be converted into proper/useful information and cannot be used to support decision making for clinical purposes. There are different automated techniques available for the earlier prediction of disease. Ensemble learning is a data analysis technique that combines multiple techniques into a single optimal predictive system to evaluate bias and variation, and to improve predictions. Diabetes data, which included 17 variables, were gathered from the UCI repository of various datasets. The predictive models used in this study include AdaBoost, Bagging, and Random Forest, to compare the precision, recall, classification accuracy, and F1-score. Finally, the Random Forest Ensemble Method had the best accuracy (97%), whereas the AdaBoost and Bagging algorithms had lower accuracy, precision, recall, and F1-scores.

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Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

Cited by 16 publications

References 53 publications

[Retracted] Considering Computational Mathematics IGHG3 as Malignant Melanoma Is Associated with Immune Infiltration of Malignant Melanoma

[Retracted] Considering Computational Mathematics IGHG3 as Malignant Melanoma Is Associated with Immune Infiltration of Malignant Melanoma

A Comprehensive Review of Various Diabetic Prediction Models: A Literature Survey

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study

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