Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence
Diabetes is a metabolic disorder in which the body is unable to properly regulate blood sugar levels. It can occur when the body does not produce enough insulin or when cells become resistant to insulin’s effects. There are two main types of diabetes, Type 1 and Type 2, which have different causes a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/15/2/85 |
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| author | Shadi AlZu’bi Mohammad Elbes Ala Mughaid Noor Bdair Laith Abualigah Agostino Forestiero Raed Abu Zitar |
| author_facet | Shadi AlZu’bi Mohammad Elbes Ala Mughaid Noor Bdair Laith Abualigah Agostino Forestiero Raed Abu Zitar |
| author_sort | Shadi AlZu’bi |
| collection | DOAJ |
| description | Diabetes is a metabolic disorder in which the body is unable to properly regulate blood sugar levels. It can occur when the body does not produce enough insulin or when cells become resistant to insulin’s effects. There are two main types of diabetes, Type 1 and Type 2, which have different causes and risk factors. Early detection of diabetes allows for early intervention and management of the condition. This can help prevent or delay the development of serious complications associated with diabetes. Early diagnosis also allows for individuals to make lifestyle changes to prevent the progression of the disease. Healthcare systems play a vital role in the management and treatment of diabetes. They provide access to diabetes education, regular check-ups, and necessary medications for individuals with diabetes. They also provide monitoring and management of diabetes-related complications, such as heart disease, kidney failure, and neuropathy. Through early detection, prevention and management programs, healthcare systems can help improve the quality of life and outcomes for people with diabetes. Current initiatives in healthcare systems for diabetes may fail due to lack of access to education and resources for individuals with diabetes. There may also be inadequate follow-up and monitoring for those who have been diagnosed, leading to poor management of the disease and lack of prevention of complications. Additionally, current initiatives may not be tailored to specific cultural or demographic groups, resulting in a lack of effectiveness for certain populations. In this study, we developed a diabetes prediction system using a healthcare framework. The system employs various machine learning methods, such as K-nearest neighbors, decision tree, deep learning, SVM, random forest, AdaBoost and logistic regression. The performance of the system was evaluated using the PIMA Indians Diabetes dataset and achieved a training accuracy of 82% and validation accuracy of 80%. |
| first_indexed | 2024-03-11T08:48:07Z |
| format | Article |
| id | doaj.art-9ec68e436a7d4995a47309f09ffc1902 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-11T08:48:07Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-9ec68e436a7d4995a47309f09ffc19022023-11-16T20:38:04ZengMDPI AGFuture Internet1999-59032023-02-011528510.3390/fi15020085Diabetes Monitoring System in Smart Health Cities Based on Big Data IntelligenceShadi AlZu’bi0Mohammad Elbes1Ala Mughaid2Noor Bdair3Laith Abualigah4Agostino Forestiero5Raed Abu Zitar6Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman 11733, JordanFaculty of Science and IT, Al-Zaytoonah University of Jordan, Amman 11733, JordanDepartment of Information Technology, Faculty of Prince Al-Hussien Bin Abdullah II for IT, The Hashemite University, P.O. Box 330127, Zarqa 13133, JordanFaculty of Science and IT, Al-Zaytoonah University of Jordan, Amman 11733, JordanHourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, JordanInstitute for High Performance Computing and Networking, National Research Council of Italy, 87036 Rende, ItalySorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi 38044, United Arab EmiratesDiabetes is a metabolic disorder in which the body is unable to properly regulate blood sugar levels. It can occur when the body does not produce enough insulin or when cells become resistant to insulin’s effects. There are two main types of diabetes, Type 1 and Type 2, which have different causes and risk factors. Early detection of diabetes allows for early intervention and management of the condition. This can help prevent or delay the development of serious complications associated with diabetes. Early diagnosis also allows for individuals to make lifestyle changes to prevent the progression of the disease. Healthcare systems play a vital role in the management and treatment of diabetes. They provide access to diabetes education, regular check-ups, and necessary medications for individuals with diabetes. They also provide monitoring and management of diabetes-related complications, such as heart disease, kidney failure, and neuropathy. Through early detection, prevention and management programs, healthcare systems can help improve the quality of life and outcomes for people with diabetes. Current initiatives in healthcare systems for diabetes may fail due to lack of access to education and resources for individuals with diabetes. There may also be inadequate follow-up and monitoring for those who have been diagnosed, leading to poor management of the disease and lack of prevention of complications. Additionally, current initiatives may not be tailored to specific cultural or demographic groups, resulting in a lack of effectiveness for certain populations. In this study, we developed a diabetes prediction system using a healthcare framework. The system employs various machine learning methods, such as K-nearest neighbors, decision tree, deep learning, SVM, random forest, AdaBoost and logistic regression. The performance of the system was evaluated using the PIMA Indians Diabetes dataset and achieved a training accuracy of 82% and validation accuracy of 80%.https://www.mdpi.com/1999-5903/15/2/85big data intelligenceclassificationdata sciencedeep learningE-healthhealthcare analytics |
| spellingShingle | Shadi AlZu’bi Mohammad Elbes Ala Mughaid Noor Bdair Laith Abualigah Agostino Forestiero Raed Abu Zitar Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence Future Internet big data intelligence classification data science deep learning E-health healthcare analytics |
| title | Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence |
| title_full | Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence |
| title_fullStr | Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence |
| title_full_unstemmed | Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence |
| title_short | Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence |
| title_sort | diabetes monitoring system in smart health cities based on big data intelligence |
| topic | big data intelligence classification data science deep learning E-health healthcare analytics |
| url | https://www.mdpi.com/1999-5903/15/2/85 |
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