A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes

Nadeem, Muhammad Waqas; Goh, Hock Guan; Ponnusamy, Vasaki; Andonović, Ivan; Khan, Muhammad Adnan; Hussain, Muzammil

doi:10.3390/healthcare9101393

Cited by 78 publications

(12 citation statements)

References 41 publications

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“…e implementation of Blockchain technology has opened up a new model of application creation which is based on the implementation of the critical data structure, the basic approach is that the data structure is related to a linked list of blocks which are mainly shared through the nodes and each node possesses an original copy of the blocks [2].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Blockchain in Enhancing the Drug Traceability in Edible Foods Using Multiple Regression Analysis

Kumar

Nassa

Uike

et al. 2022

Journal of Food Quality

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The growing need for access to safer food items is increasing, and hence, there is a need for a better supply chain management system in the food industry is increasing. The increased complexity of the existing systems tends to introduce more issues to the stakeholders, and also, the cost of product traceability is quite high. Hence, the industry is looking for effective solutions in relation to drug traceability, and the application of Blockchain technology enables the stakeholders in the food and beverage (F&B) sector to track the movement of goods, supported in gathering the required details so that the contaminated products can be identified and recalled without much delay and lesser recall costs to protect the lives of the individuals. The tampered food items are increasing and are impacting the supply chain process, brand name of the companies, and claim assurance. They create an adverse impact on the health of the individuals and cause higher economic loss to the health-care industry. The existing studies tend to focus on laying emphasis of the need for an enhanced, effective, and end tracking systems in the industry. The emergence of Blockchain technology enables centralized tracking of information support in enhancing the data privacy and increasing transparency and support in eradicating the tampered food products in the supply chain system. These approaches leverage the usage of smart contracts and decentralize the storage of information in a secure manner for enhanced product traceability in the F&B industry. The implementation of smart contracts generates better data governance, which tends to meet the needs and requirements of the stakeholders, and applies effective measures of food traceability. The primary objective of the study is to perform an analysis of Blockchain in enhancing drug traceability in the food sector. The researcher uses quantitative analysis for the study as it helps in understanding the critical determinants influencing drug traceability in food effectively, the survey method is used to gather the information, and past reviews are also used to possess a better understanding of the subject area effectively.

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Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Blockchain in Enhancing the Drug Traceability in Edible Foods Using Multiple Regression Analysis

Kumar

Nassa

Uike

et al. 2022

Journal of Food Quality

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“…Now, individuals need to take proper insulin injections and prescribed tablets to maintain a proper lifestyle. Hence, patients who possess these diseases tend to generate more amount of information which are related to the disease, the type of medications currently taken in medical records, insurance, measurement of blood glucose, home measures of other critical vitals, blood pressure monitoring, and other key metrics [2]. ese information are highly critical and need to be stored in a safe place, so that it can be retrieved as and when required by patients, medical practitioners, nurses, and healthcare centers for making better healthcare decisions.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Diabetes Care in Elder Individuals Using Artificial Intelligence

Vyas

Gupta

Tarar

et al. 2022

Journal of Food Quality

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The term blockchain is mainly regarded as the distributed transaction which is mainly comprised of different blocks, and each set tends to represent the data that are being associated with the previous blocks. The blockchain is mainly managed through peer-to-peer networks which comparatively involves in adhering to the protocol of authenticating various blocks to form the blockchain. The usage of blockchain technology has been increasingly used in different fields, and healthcare services are now using blockchain for better patient delivery, detecting disease, and other aspects. The scope of the proposed study is that this study has exploited the function of a blockchain-enabled big data network to support medical professionals in giving better treatment modalities and delivering better patient care. The application of a new generation of smart block chains such as Ethereum and NEM is now offering better services and features in creating blockchain-based healthcare data management and hence support healthcare centers, medical practitioners, nurses, radiologists, and patients for better healthcare management. The application of blockchain technology in big data networks supports adding more value as it results in enhanced data quality, accessibility, and support in creating better security and safety of data and information, which is highly essential in the medical industry. Blockchain technology enables big data technologies enabled in supporting medical practitioners in addressing various healthcare ailments; one of the major diseases impacting many people around the world is diabetes. Patients with such ailments tend to generate more data and information related to the disease and health-related aspects. Hence, this information requires being maintained and analyzed, so that superior healthcare services can be provided. This study is more involved in the investigation of blockchain technology through a big data network enabled in offering better care for elderly individuals who have been affected due to diabetes, the researchers propose to choose a questionnaire method to collect the data from nearly 169 respondents, and these data were then analyzed using SPSS data package. The analyst used percentage analysis, correlation analysis, and chi-square test to analyze the data which has been collated by the researchers. The results and discussion show in detail the major aspects of blockchain technology in supporting healthcare professionals for better diabetes care management for elderly individuals.

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“…Recall indicates what proportion of events that actually was of a certain class was classified by us as that class. It is the division of the true positives to all positives, as shown in Equation (3) [ 24 ]:

…”

Section: Steps and Methodsmentioning

confidence: 99%

Predicting the Onset of Diabetes with Machine Learning Methods

Chou

Hsu

Chou

2023

JPM

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The number of people suffering from diabetes in Taiwan has continued to rise in recent years. According to the statistics of the International Diabetes Federation, about 537 million people worldwide (10.5% of the global population) suffer from diabetes, and it is estimated that 643 million people will develop the condition (11.3% of the total population) by 2030. If this trend continues, the number will jump to 783 million (12.2%) by 2045. At present, the number of people with diabetes in Taiwan has reached 2.18 million, with an average of one in ten people suffering from the disease. In addition, according to the Bureau of National Health Insurance in Taiwan, the prevalence rate of diabetes among adults in Taiwan has reached 5% and is increasing each year. Diabetes can cause acute and chronic complications that can be fatal. Meanwhile, chronic complications can result in a variety of disabilities or organ decline. If holistic treatments and preventions are not provided to diabetic patients, it will lead to the consumption of more medical resources and a rapid decline in the quality of life of society as a whole. In this study, based on the outpatient examination data of a Taipei Municipal medical center, 15,000 women aged between 20 and 80 were selected as the subjects. These women were patients who had gone to the medical center during 2018–2020 and 2021–2022 with or without the diagnosis of diabetes. This study investigated eight different characteristics of the subjects, including the number of pregnancies, plasma glucose level, diastolic blood pressure, sebum thickness, insulin level, body mass index, diabetes pedigree function, and age. After sorting out the complete data of the patients, this study used Microsoft Machine Learning Studio to train the models of various kinds of neural networks, and the prediction results were used to compare the predictive ability of the various parameters for diabetes. Finally, this study found that after comparing the models using two-class logistic regression as well as the two-class neural network, two-class decision jungle, or two-class boosted decision tree for prediction, the best model was the two-class boosted decision tree, as its area under the curve could reach a score of 0.991, which was better than other models.

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A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes

Cited by 78 publications

References 41 publications

A Comparative Analysis of Blockchain in Enhancing the Drug Traceability in Edible Foods Using Multiple Regression Analysis

A Comparative Analysis of Blockchain in Enhancing the Drug Traceability in Edible Foods Using Multiple Regression Analysis

Investigation of Diabetes Care in Elder Individuals Using Artificial Intelligence

Predicting the Onset of Diabetes with Machine Learning Methods

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