IoT Based Expert System for Diabetes Diagnosis and Insulin Dosage Calculation
High blood glucose levels are the defining characteristic of diabetes. Uncontrolled blood glucose levels in diabetic patients might result in mortality. As a result, there is a dire need to control blood glucose levels by constantly monitoring them and delivering the appropriate amount of insulin. H...
Saved in:
Published in: | Healthcare (Basel) 2022-12, Vol.11 (1), p.12 |
---|---|
Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c458t-a5525f440c4c6a2d9d96c0b14b7ed3209a94dcc14804ad1a64e220722c8e879e3 |
---|---|
cites | cdi_FETCH-LOGICAL-c458t-a5525f440c4c6a2d9d96c0b14b7ed3209a94dcc14804ad1a64e220722c8e879e3 |
container_end_page | |
container_issue | 1 |
container_start_page | 12 |
container_title | Healthcare (Basel) |
container_volume | 11 |
creator | Valsalan, Prajoona Hasan, Najam Ul Farooq, Umer Zghaibeh, Manaf Baig, Imran |
description | High blood glucose levels are the defining characteristic of diabetes. Uncontrolled blood glucose levels in diabetic patients might result in mortality. As a result, there is a dire need to control blood glucose levels by constantly monitoring them and delivering the appropriate amount of insulin. However, insulin consumption is affected by several variables, including age, calorific intake, and body weight. The patient must see the doctor on a regular basis in order to determine the appropriate dose. Nonetheless, hospital facilities are finding it increasingly difficult to treat patients as the number of patients rises; thus, the healthcare industry is searching for an efficient method that can alleviate their burden by assisting patients with chronic conditions through remote patient care. In this work, we have developed an expert system to provide remote treatment for diabetic patients. Our expert system consists of two distinct components: one for the patient and one for the hospital. The sole requirement for the patient will be a wearable device that captures and transmits all relevant data to the cloud. On the hospital side, there should be a system in place to process that data in the cloud. The system employs a fuzzy system to handle data in two stages. A fuzzy system is initially employed to identify whether or not a patient is diabetic. In the second stage, a fuzzy system is utilized to determine the insulin dosage for a diabetic patient. Using sensors and the ESP8266 platform, we have developed a prototype of patient-side hardware. The MATLAB fuzzy toolbox is used for the processing part, which includes fuzzy systems, and the results of the MATLAB analysis are presented in the form of simulation results to demonstrate the accuracy of the proposed system in terms of determining insulin dosage. The results of the simulation using the fuzzy toolbox for the insulin dose of the diabetic patient are significantly close to the amount of dosage prescribed by the endocrinologist. |
doi_str_mv | 10.3390/healthcare11010012 |
format | article |
fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9819254</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A744639244</galeid><sourcerecordid>A744639244</sourcerecordid><originalsourceid>FETCH-LOGICAL-c458t-a5525f440c4c6a2d9d96c0b14b7ed3209a94dcc14804ad1a64e220722c8e879e3</originalsourceid><addsrcrecordid>eNplkU1PGzEQhq2qFSDKH-BQWeqFS8Bfu15fKkGANhIVB-BsTbyziZFjB3sXlX_fDd-lPtgj-ZnHHr2E7HN2KKVhR0uE0C8dZOSccca4-ER2hBB6YpgUn9_V22SvlFs2LsNlI6stsi3rmnOlxQ75PUvX9AQKtvTszxpzT68eSo8r2qVMTz3MsceyKRYxFV8oxJbOYhmCj_Q0FVggnUJwQ4Dep_iVfOkgFNx7PnfJzfnZ9fTX5OLy52x6fDFxqmr6CVSVqDqlmFOuBtGa1tSOzbmaa2ylYAaMap3jqmEKWg61QiGYFsI12GiDcpf8ePKuh_kKW4exzxDsOvsV5AebwNt_b6Jf2kW6t6bhRlRqFBw8C3K6G7D0duWLwxAgYhqKFbrmRutGbdDvH9DbNOQ4jvdI8Wrc9Bu1gIDWxy6N77qN1B5rpWppxKNLPFEup1Iydq9f5sxuYrX_xzo2fXs_7GvLS4jyL7kenpA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2761157617</pqid></control><display><type>article</type><title>IoT Based Expert System for Diabetes Diagnosis and Insulin Dosage Calculation</title><source>Publicly Available Content Database</source><source>PubMed Central</source><source>Coronavirus Research Database</source><creator>Valsalan, Prajoona ; Hasan, Najam Ul ; Farooq, Umer ; Zghaibeh, Manaf ; Baig, Imran</creator><creatorcontrib>Valsalan, Prajoona ; Hasan, Najam Ul ; Farooq, Umer ; Zghaibeh, Manaf ; Baig, Imran</creatorcontrib><description>High blood glucose levels are the defining characteristic of diabetes. Uncontrolled blood glucose levels in diabetic patients might result in mortality. As a result, there is a dire need to control blood glucose levels by constantly monitoring them and delivering the appropriate amount of insulin. However, insulin consumption is affected by several variables, including age, calorific intake, and body weight. The patient must see the doctor on a regular basis in order to determine the appropriate dose. Nonetheless, hospital facilities are finding it increasingly difficult to treat patients as the number of patients rises; thus, the healthcare industry is searching for an efficient method that can alleviate their burden by assisting patients with chronic conditions through remote patient care. In this work, we have developed an expert system to provide remote treatment for diabetic patients. Our expert system consists of two distinct components: one for the patient and one for the hospital. The sole requirement for the patient will be a wearable device that captures and transmits all relevant data to the cloud. On the hospital side, there should be a system in place to process that data in the cloud. The system employs a fuzzy system to handle data in two stages. A fuzzy system is initially employed to identify whether or not a patient is diabetic. In the second stage, a fuzzy system is utilized to determine the insulin dosage for a diabetic patient. Using sensors and the ESP8266 platform, we have developed a prototype of patient-side hardware. The MATLAB fuzzy toolbox is used for the processing part, which includes fuzzy systems, and the results of the MATLAB analysis are presented in the form of simulation results to demonstrate the accuracy of the proposed system in terms of determining insulin dosage. The results of the simulation using the fuzzy toolbox for the insulin dose of the diabetic patient are significantly close to the amount of dosage prescribed by the endocrinologist.</description><identifier>ISSN: 2227-9032</identifier><identifier>EISSN: 2227-9032</identifier><identifier>DOI: 10.3390/healthcare11010012</identifier><identifier>PMID: 36611472</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Biosensors ; Blood sugar monitoring ; Chronic illnesses ; Cloud computing ; Coronaviruses ; COVID-19 ; Decision making ; Diabetes ; Diagnosis ; Disease ; Dosage and administration ; Drug therapy ; Fuzzy control ; Fuzzy logic ; Health care policy ; Health facilities ; Health services ; Insulin ; Internet of Things ; Medical diagnosis ; Older people ; Pandemics ; Patients ; Physicians ; Sensors ; Smartphones ; Technology application ; Telemedicine</subject><ispartof>Healthcare (Basel), 2022-12, Vol.11 (1), p.12</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c458t-a5525f440c4c6a2d9d96c0b14b7ed3209a94dcc14804ad1a64e220722c8e879e3</citedby><cites>FETCH-LOGICAL-c458t-a5525f440c4c6a2d9d96c0b14b7ed3209a94dcc14804ad1a64e220722c8e879e3</cites><orcidid>0000-0002-3142-6958 ; 0000-0001-6906-8802 ; 0000-0002-5220-4908 ; 0000-0002-0284-5600 ; 0000-0002-9459-9290</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2761157617/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2761157617?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25731,27901,27902,36989,36990,38493,43871,44566,53766,53768,74382,75096</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36611472$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Valsalan, Prajoona</creatorcontrib><creatorcontrib>Hasan, Najam Ul</creatorcontrib><creatorcontrib>Farooq, Umer</creatorcontrib><creatorcontrib>Zghaibeh, Manaf</creatorcontrib><creatorcontrib>Baig, Imran</creatorcontrib><title>IoT Based Expert System for Diabetes Diagnosis and Insulin Dosage Calculation</title><title>Healthcare (Basel)</title><addtitle>Healthcare (Basel)</addtitle><description>High blood glucose levels are the defining characteristic of diabetes. Uncontrolled blood glucose levels in diabetic patients might result in mortality. As a result, there is a dire need to control blood glucose levels by constantly monitoring them and delivering the appropriate amount of insulin. However, insulin consumption is affected by several variables, including age, calorific intake, and body weight. The patient must see the doctor on a regular basis in order to determine the appropriate dose. Nonetheless, hospital facilities are finding it increasingly difficult to treat patients as the number of patients rises; thus, the healthcare industry is searching for an efficient method that can alleviate their burden by assisting patients with chronic conditions through remote patient care. In this work, we have developed an expert system to provide remote treatment for diabetic patients. Our expert system consists of two distinct components: one for the patient and one for the hospital. The sole requirement for the patient will be a wearable device that captures and transmits all relevant data to the cloud. On the hospital side, there should be a system in place to process that data in the cloud. The system employs a fuzzy system to handle data in two stages. A fuzzy system is initially employed to identify whether or not a patient is diabetic. In the second stage, a fuzzy system is utilized to determine the insulin dosage for a diabetic patient. Using sensors and the ESP8266 platform, we have developed a prototype of patient-side hardware. The MATLAB fuzzy toolbox is used for the processing part, which includes fuzzy systems, and the results of the MATLAB analysis are presented in the form of simulation results to demonstrate the accuracy of the proposed system in terms of determining insulin dosage. The results of the simulation using the fuzzy toolbox for the insulin dose of the diabetic patient are significantly close to the amount of dosage prescribed by the endocrinologist.</description><subject>Biosensors</subject><subject>Blood sugar monitoring</subject><subject>Chronic illnesses</subject><subject>Cloud computing</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Decision making</subject><subject>Diabetes</subject><subject>Diagnosis</subject><subject>Disease</subject><subject>Dosage and administration</subject><subject>Drug therapy</subject><subject>Fuzzy control</subject><subject>Fuzzy logic</subject><subject>Health care policy</subject><subject>Health facilities</subject><subject>Health services</subject><subject>Insulin</subject><subject>Internet of Things</subject><subject>Medical diagnosis</subject><subject>Older people</subject><subject>Pandemics</subject><subject>Patients</subject><subject>Physicians</subject><subject>Sensors</subject><subject>Smartphones</subject><subject>Technology application</subject><subject>Telemedicine</subject><issn>2227-9032</issn><issn>2227-9032</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><recordid>eNplkU1PGzEQhq2qFSDKH-BQWeqFS8Bfu15fKkGANhIVB-BsTbyziZFjB3sXlX_fDd-lPtgj-ZnHHr2E7HN2KKVhR0uE0C8dZOSccca4-ER2hBB6YpgUn9_V22SvlFs2LsNlI6stsi3rmnOlxQ75PUvX9AQKtvTszxpzT68eSo8r2qVMTz3MsceyKRYxFV8oxJbOYhmCj_Q0FVggnUJwQ4Dep_iVfOkgFNx7PnfJzfnZ9fTX5OLy52x6fDFxqmr6CVSVqDqlmFOuBtGa1tSOzbmaa2ylYAaMap3jqmEKWg61QiGYFsI12GiDcpf8ePKuh_kKW4exzxDsOvsV5AebwNt_b6Jf2kW6t6bhRlRqFBw8C3K6G7D0duWLwxAgYhqKFbrmRutGbdDvH9DbNOQ4jvdI8Wrc9Bu1gIDWxy6N77qN1B5rpWppxKNLPFEup1Iydq9f5sxuYrX_xzo2fXs_7GvLS4jyL7kenpA</recordid><startdate>20221221</startdate><enddate>20221221</enddate><creator>Valsalan, Prajoona</creator><creator>Hasan, Najam Ul</creator><creator>Farooq, Umer</creator><creator>Zghaibeh, Manaf</creator><creator>Baig, Imran</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7XB</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>KB0</scope><scope>M2O</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PIMPY</scope><scope>PJZUB</scope><scope>PKEHL</scope><scope>PPXIY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3142-6958</orcidid><orcidid>https://orcid.org/0000-0001-6906-8802</orcidid><orcidid>https://orcid.org/0000-0002-5220-4908</orcidid><orcidid>https://orcid.org/0000-0002-0284-5600</orcidid><orcidid>https://orcid.org/0000-0002-9459-9290</orcidid></search><sort><creationdate>20221221</creationdate><title>IoT Based Expert System for Diabetes Diagnosis and Insulin Dosage Calculation</title><author>Valsalan, Prajoona ; Hasan, Najam Ul ; Farooq, Umer ; Zghaibeh, Manaf ; Baig, Imran</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c458t-a5525f440c4c6a2d9d96c0b14b7ed3209a94dcc14804ad1a64e220722c8e879e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Biosensors</topic><topic>Blood sugar monitoring</topic><topic>Chronic illnesses</topic><topic>Cloud computing</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Decision making</topic><topic>Diabetes</topic><topic>Diagnosis</topic><topic>Disease</topic><topic>Dosage and administration</topic><topic>Drug therapy</topic><topic>Fuzzy control</topic><topic>Fuzzy logic</topic><topic>Health care policy</topic><topic>Health facilities</topic><topic>Health services</topic><topic>Insulin</topic><topic>Internet of Things</topic><topic>Medical diagnosis</topic><topic>Older people</topic><topic>Pandemics</topic><topic>Patients</topic><topic>Physicians</topic><topic>Sensors</topic><topic>Smartphones</topic><topic>Technology application</topic><topic>Telemedicine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Valsalan, Prajoona</creatorcontrib><creatorcontrib>Hasan, Najam Ul</creatorcontrib><creatorcontrib>Farooq, Umer</creatorcontrib><creatorcontrib>Zghaibeh, Manaf</creatorcontrib><creatorcontrib>Baig, Imran</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest Health & Medical Research Collection</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Health & Nursing</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Healthcare (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Valsalan, Prajoona</au><au>Hasan, Najam Ul</au><au>Farooq, Umer</au><au>Zghaibeh, Manaf</au><au>Baig, Imran</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>IoT Based Expert System for Diabetes Diagnosis and Insulin Dosage Calculation</atitle><jtitle>Healthcare (Basel)</jtitle><addtitle>Healthcare (Basel)</addtitle><date>2022-12-21</date><risdate>2022</risdate><volume>11</volume><issue>1</issue><spage>12</spage><pages>12-</pages><issn>2227-9032</issn><eissn>2227-9032</eissn><abstract>High blood glucose levels are the defining characteristic of diabetes. Uncontrolled blood glucose levels in diabetic patients might result in mortality. As a result, there is a dire need to control blood glucose levels by constantly monitoring them and delivering the appropriate amount of insulin. However, insulin consumption is affected by several variables, including age, calorific intake, and body weight. The patient must see the doctor on a regular basis in order to determine the appropriate dose. Nonetheless, hospital facilities are finding it increasingly difficult to treat patients as the number of patients rises; thus, the healthcare industry is searching for an efficient method that can alleviate their burden by assisting patients with chronic conditions through remote patient care. In this work, we have developed an expert system to provide remote treatment for diabetic patients. Our expert system consists of two distinct components: one for the patient and one for the hospital. The sole requirement for the patient will be a wearable device that captures and transmits all relevant data to the cloud. On the hospital side, there should be a system in place to process that data in the cloud. The system employs a fuzzy system to handle data in two stages. A fuzzy system is initially employed to identify whether or not a patient is diabetic. In the second stage, a fuzzy system is utilized to determine the insulin dosage for a diabetic patient. Using sensors and the ESP8266 platform, we have developed a prototype of patient-side hardware. The MATLAB fuzzy toolbox is used for the processing part, which includes fuzzy systems, and the results of the MATLAB analysis are presented in the form of simulation results to demonstrate the accuracy of the proposed system in terms of determining insulin dosage. The results of the simulation using the fuzzy toolbox for the insulin dose of the diabetic patient are significantly close to the amount of dosage prescribed by the endocrinologist.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>36611472</pmid><doi>10.3390/healthcare11010012</doi><orcidid>https://orcid.org/0000-0002-3142-6958</orcidid><orcidid>https://orcid.org/0000-0001-6906-8802</orcidid><orcidid>https://orcid.org/0000-0002-5220-4908</orcidid><orcidid>https://orcid.org/0000-0002-0284-5600</orcidid><orcidid>https://orcid.org/0000-0002-9459-9290</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2227-9032 |
ispartof | Healthcare (Basel), 2022-12, Vol.11 (1), p.12 |
issn | 2227-9032 2227-9032 |
language | eng |
recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9819254 |
source | Publicly Available Content Database; PubMed Central; Coronavirus Research Database |
subjects | Biosensors Blood sugar monitoring Chronic illnesses Cloud computing Coronaviruses COVID-19 Decision making Diabetes Diagnosis Disease Dosage and administration Drug therapy Fuzzy control Fuzzy logic Health care policy Health facilities Health services Insulin Internet of Things Medical diagnosis Older people Pandemics Patients Physicians Sensors Smartphones Technology application Telemedicine |
title | IoT Based Expert System for Diabetes Diagnosis and Insulin Dosage Calculation |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-17T00%3A33%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=IoT%20Based%20Expert%20System%20for%20Diabetes%20Diagnosis%20and%20Insulin%20Dosage%20Calculation&rft.jtitle=Healthcare%20(Basel)&rft.au=Valsalan,%20Prajoona&rft.date=2022-12-21&rft.volume=11&rft.issue=1&rft.spage=12&rft.pages=12-&rft.issn=2227-9032&rft.eissn=2227-9032&rft_id=info:doi/10.3390/healthcare11010012&rft_dat=%3Cgale_pubme%3EA744639244%3C/gale_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c458t-a5525f440c4c6a2d9d96c0b14b7ed3209a94dcc14804ad1a64e220722c8e879e3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2761157617&rft_id=info:pmid/36611472&rft_galeid=A744639244&rfr_iscdi=true |