International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 02 | Feb 2024 www.irjet.net p-ISSN:2395-0072
Exciting Advancements in Artificial Intelligence and Machine Learning for Type 2 Diabetes
Kaiser Jamil1, Mohammed Asimuddin2 Iqra Jabeen2 Hajira Fatima1 and Mohamad Al Saiqali3
1Head of Genetics Department, Bhagwan Mahavir Medical Research Centre, 10-1-1, Mahavir Marg, Hyderabad-500004, Telangana, India
2Senior Scientist, Prof. G.M.Reddy Research Foundation (GMRRF) Hyderabad-500039, Telangana, India
3Associate Professor, Microbiology Department, Anwarul Uloom College, Hyderabad, TS, India ***
Abstract
Type2diabetesoftenreferredtoasadult-onsetdiabetes,isachronicmetabolicdisordercharacterizedbyelevatedlevelsof bloodsugarorglucose.Thisconditionoccurswhenthebody'scellsbecomeresistanttotheeffectsofinsulin,ahormonethat regulates blood sugar levels. As a result, the pancreas must produce more insulin to compensate, leading to a state of hyperinsulinemia.Overtime,thepancreasmaystruggletokeepupwiththisincreaseddemand,and bloodsugarlevelscan become uncontrolled. Hence it is obvious that Type2 diabetes is a complex condition,also influenced by both geneticand lifestyle factors, such as diet, physical activity, and obesity. It is a significant global health concern, affecting millions of people worldwide. Left untreated or poorly managed, type 2 diabetes can lead to serious health complications, including heart disease, kidney problems, nerve damage, and vision impairment. Artificial Intelligence (AI) is a new and fast developingapplicationtooltohealthcaresystem,itcanreformourapproachtodiagnosisandmanagediabetes.Sincemuch hasbeensaidandresearchedintheareaof Diabetes,thisarticlepointstonewerdirectionswhereapplicationofArtificial Intelligence and Machine learning (AI and ML) can provide better diagnosis for this chronic illness. These newer search methods of compiling big data using AI and ML tools have shown great promise The aim of this review is an attempt to better comprehend how AI and ML developments may be useful to individuals with diabetes AI and ML approaches are beingusedtoanalysevastdatasets,suchaselectronichealthrecordsandgeneticinformation,toidentifynovelriskfactorsto predictdiabetesoutcomes,andimprovetreatmentstrategies.
Keywords:Artificialintelligence(AI),Machinelearning(ML),Diabetes,Diseaseprediction
Introduction
Artificial Intelligence (AI) and Machine Learning (MI) in Health care
In the ever-evolving landscape of healthcare, Artificial Intelligence (AI) and Machine Learning (ML) are becoming increasingly pivotal in the diagnosis, management, and treatment of chronic diseases1 . One such disease, Type 2 diabetes (T2D), has witnessed remarkable advancements thanks to these technologies. This review explores the thrilling progress and potential that AI and ML hold for the Type 2 diabetes landscape. For example T2D is also referred to as Metabolic Syndrome hence the metabolites produced by a cell, tissue or organs represent the endpoint of the omics process. Metabolomicsworkflowscan beusedtoidentifynewand unknown compounds.Furtherrisk assessmentcan be predicted usingthesemoderntoolssuchasMLandAI2 .
Exciting Frontiers in Early Detection:
The early detection of Type 2 diabetes is a game-changer, and AI and ML have risen to the challenge. Machine learning algorithms can sift through vast datasets, identifying subtle patterns and risk factors that may go unnoticed by traditional methods. These AI-powered systems enable healthcare professionals to make precise predictions about a patient's susceptibility to the disease, thereby facilitating early intervention and prevention strategies. In the case of T2D it is essentialtounderstandthedisease3 andsearchforpotentialbiomarkers Thisnotonlysaveslivesbutalsoreducesthelongterm burden on healthcare systems. ML algorithmscananalyze patient data,suchas medical records, geneticinformation, andlifestylefactors,toidentifyindividualsathighriskofdevelopingType2Diabetes.AIcanhelpinautomatingtheanalysis ofmedicalimages(e.g.,retinalscans,MRI)todetectearlysignsofdiabetes-relatedcomplications,likediabeticretinopathy. Acomplicationduetolong-termdiabeticdiseaseisshowninFigure-1below:
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 02 | Feb 2024 www.irjet.net p-ISSN:2395-0072
Figure-1: Source-GoogleImagesofDiabetes,Showingthevariouscomplicationsduetodiabetes. Real-time Monitoring and decision support can be achieved through ML models which can continuously monitor patient data,providingreal-timeinsightsintotheirhealthstatus.Thus,decisionsupportsystemscanassisthealthcareprofessionals inadaptingtreatmentplansbasedonevolvingpatientconditions.
Personalized Treatment Plans:
AIandMLhaveusheredinaneweraofpersonalizedmedicineforType2diabetespatients.Byanalyzingapatient'sgenetic makeup,lifestylefactors,andmedicalhistory,thesetechnologiescantailortreatmentplanstosuitindividualneeds.Through continuous monitoring and real-time data analysis, AI-driven tools can adapt treatment strategies on the fly, ensuring optimalglycemiccontrolandminimizingtheriskofcomplications.Theresultisaremarkableimprovementinthequalityof life for those living with Type2 diabetes. Diagnosis of type-2 diabetes and generatingbigdata cansetthe stage forAI and buildstandardsforbestmodelsinMLandAI4 .MLcanbeusedtotailortreatmentplansforindividualsbasedontheirunique characteristicsandresponsestomedications.Thiscanhelpoptimizebloodsugarcontrolandreducetheriskofsideeffects. AI-poweredchatbotsandvirtualassistantscanprovidereal-timeadviceandsupporttopatients,helpingthemmanagetheir conditionmoreeffectively. PatientsupportandtheircooperationinbuildingupAlprogramsandusingMLtechniqueshas been the backbone of this venture5 ML models can assess an individual's risk of developing certain diseases based on genetic and environmental factors. This information allows for proactive interventions and personalized prevention strategies.
Precision Medicine:
Thetraditional"one-size-fits-all"approachtomanagingType2diabetesisrapidlybecomingobsolete6,7,8 AIandMLhave enabled the development of precision medicine, where treatments are fine-tuned to each patient's specific requirements. This not only results in improved therapeutic outcomes but also minimizes side effects and complications, making management more effective and patient-centric. Researchers were increasingly focusing on individualized treatment and
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 02 | Feb 2024 www.irjet.net p-ISSN:2395-0072
prevention strategies based on a person's genetic and metabolic profile. This allows for more targeted and effective therapies for managing Type 2 diabetes. ML algorithms can help optimize insulin dosing regimens for individuals, taking into account factors like diet, activity level, and daily routines. AI can also assist in medication adherence by sending remindersandeducationalcontenttopatients.Further,MLmodelscanintegrateandanalyzediverseclinicaldata,including electronichealthrecords(EHRs),imagingdata,andpatienthistories.
Table-1: ShowingtheBuildingofapredictivemodelforadisease(Diabetes)usingbigdatainvolvesseveralsteps. Presentingahigh-leveloverviewoftheprocess:
Step
1.DefinetheProblem
2.GatherData
3.DataPreprocessing
4.ExploratoryDataAnalysis (EDA)
5.FeatureEngineering
6.ModelSelection
7.ModelTraining
8.EvaluationMetrics
9.ModelInterpretability
10.ValidationandTesting
11.Deployment
Description
Clearlydefinetheproblemtobesolved(e.g.,predictingdiseaserisk).
Collectadiversedatasetwithrelevantfeatures,ensuringdataquality andprivacycompliance.
Cleanandpreprocessthedata,handlingmissingvalues,outliers,and inconsistencies.
Analyzeandvisualizethedatatounderstandpatterns,correlations,and relationships.
Createortransformfeaturestoimprovemodelperformance.
Chooseasuitablemachinelearningalgorithmbasedontheproblemand datacharacteristics.
Splitthedataset,trainthemodelonthetrainingset,andoptimize hyperparameters.
Selectappropriatemetrics(e.g.,accuracy,precision,recall,AUC-ROC)for modelevaluation.
Ensurethemodelisinterpretable,aligningwithmedicalknowledge.
Performcross-validationandtestthemodelonnew,unseendatafor generalization.
Deploythemodelinareal-worldsettingwithmonitoringmechanisms.
12.ContinuousImprovement Regularlyupdatethemodelwithnewdata,monitorperformance,and retrainasneeded.
Important consideration include: Ethical and Regulatory Compliance standards and healthcare regulations, besides protecting sensitive patient information. At every step involvement healthcare experts for clinical relevance is utmost important By considering multiple data sources, AI can provide a comprehensive view of an individual's health status and help in making more informed treatment decisions. This enables the identification of the most effective and least harmful treatmentsforindividualpatients,reducingtrialanderrorintreatmentselection.
Predictive Analytics:
Predictive analytics powered by AI and ML have revolutionized the management of Type 2 diabetes. By continuously monitoringvitalhealthparameters,thesesystemscanpredictglucosefluctuationsandothercriticaleventswellinadvance. This enables patients and healthcare professionals to take proactive measures, thus averting crises and preventing complications9 This technology has the potential to transform the way we manage diabetes, ensuring better health outcomes and reduced hospitalization rates. ML models can forecast blood sugar levels and the risk of complications, allowing healthcare providers to intervene proactively. Building predictive models has been a mammoth task, but much successhasbeenachievedwithscopeforfurtherimprovement10
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 02 | Feb 2024 www.irjet.net p-ISSN:2395-0072
Table-2: OutliningtheimpactofpredictiveanalyticspoweredbyAIandMLonthemanagementofType2diabetes:
Aspect
EarlyDetectionand Diagnosis
PersonalizedTreatment Plans
ContinuousMonitoring
RiskStratification
ImprovedMedication Adherence
Lifestyle Recommendations
Data-DrivenInsightsfor HealthcareProviders
Description
Predictive analytics enable early identification of individuals at risk of developing Type 2 diabetes based on various factors such as genetics, lifestyle,andhealthdata.Thisallowsfortimelyinterventionandpreventive measures.
AIandML algorithmsanalyzepatient data tocreatepersonalized treatment plans,consideringfactorslikeblood glucoselevels,lifestyle,and medication response. This tailored approach improves the effectiveness of diabetes management.
Smart devices and wearables equipped with AI-powered analytics provide real-time monitoring of glucose levels, physical activity, and other relevant metrics. This continuous feedback helps patients and healthcare providers makeinformeddecisions.
Predictive models stratify patients based on their risk of complications, allowing healthcare professionals to prioritize resources and interventions forthoseathigherrisk,thusoptimizinghealthcaredelivery.
AI applications can predict patient adherence to medication regimens by analyzing historical data and patterns. This insight allows healthcare providers to address non-adherence issues promptly, improving overall treatmentoutcomes.
AI-drivenanalyticsconsiderlifestylefactorssuchasdietandexercisehabits to offer personalized recommendations. This holistic approach supports patients in making sustainable lifestyle changes to better manage their diabetes.
HealthcareprofessionalsbenefitfromAI-generatedinsights,facilitatingdatadriven decision-making. Predictive analytics assist in identifying trends, optimizingresourceallocation,andimprovingoverallpatientoutcomes.
Somemodelscanalsopredicttheimpactoflifestylechanges(e.g.,dietandexercise)onbloodsugarcontrol.Further,building newmodelsusingbigdataforkidneydiseaseorotherdiseasesispredicted11
Enhanced Patient Engagement:
One of the most exciting aspects of AI and ML in Type 2 diabetes is their ability to engage patients actively in their care. Throughwearabledevices,smartphoneapps,andpatientportals,individualscanmonitortheirhealthandreceivereal-time feedback. This engagement not only motivates patients to adhere to their treatment plans but also fosters a sense of empowermentandautonomyinmanagingtheircondition.AIhassignificantpotentialtoimprovescreening,diagnosis,and managementofpatientswithdiabetes.Wearabledevicesandcontinuousglucosemonitorscancollectreal-timedata,which AIcananalyzetoprovidealertsforbothpatientsandhealthcareproviderswhenbloodsugarlevelsareoutofrange.AIcan alsodetectpatternsinglucosedata,helpingtoidentifytrendsandpotentialcausesofbloodsugarfluctuations.AIcananalyze apatient'sdietarychoicesandphysicalactivitytoofferpersonalizedrecommendationsforhealthierhabits. Mobileappscan help individuals track their food intake, exercise, and other lifestyle factors and provide guidance based on this data. Most importantly, AI can contribute to ensuring the security and privacy of patient data, a crucial aspect of precision medicine wheresensitiveinformationisinvolved.
Data Integration and Electronic Health Records (EHR):
AIcan acceleratedrug discovery by analyzing vast datasets to identify potential targets, biomarkers, and novel therapeutic compoundsfordiabetes.MLcanoptimizeclinicaltrialdesignandpatientselection,potentiallyspeedingupthedevelopment of new treatments.AI can help integrate and make sense of vast amounts of patient data from electronic health records,
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 02 | Feb 2024 www.irjet.net p-ISSN:2395-0072
facilitatingresearchandpatientcare 12 .Hencewhatweforeseehasbeenbeautifullydescribedtakingintoconsiderationthe role of endocrine or the hormonal status in diabetics 13 It can assist in identifying relationships between various health parametersanddiabetesoutcomes.
Remote Monitoring:
Telemedicineplatforms,oftenutilizingAIforremotemonitoring,enablehealthcareproviderstotrackpatients'progressand intervene as necessary, reducing the need for frequent in-person visits. AI and ML have the potential to significantly enhance our understanding of Type 2 Diabetes, improve patient outcomes, and streamline healthcare processes. High performance tools like ML and AI are in, and new modes of treatment plans are at our door step14 medical However, it's crucialtoensurethatthesetechnologiesareintegratedintohealthcare systemswithappropriatedataprivacyandsecurity measures,andthattheyarevalidatedthroughrigorousclinicaltrialstoensuretheirsafetyandeffectiveness. Thefutureof diabeticcareisaroundthecorner15
Conclusion:
TheintegrationofArtificialIntelligenceandMachineLearningintherealmofType2diabetesisnothingshortofthrilling15 Thepromiseofearlydetection,personalizedtreatmentplans,precisionmedicine,predictiveanalytics,andenhancedpatient engagementistransformingthelandscapeofdiabetescare.Thesetechnologieshavethepotentialtoimprovethequalityof lifeformillionsofindividualslivingwithType2diabetesandalleviatetheburdenonhealthcaresystemsworldwide.AsAI and ML continue to evolve, the future holds exciting prospects for more efficient, effective, and patient-centred diabetes management.AIandMLapplicationshavethecapabilitiestotransformdiabetescareandhelpmillionsofdiabeticpeopleto achieve better blood glucose control,reduce hypoglycemic episodes, and reduce diabetes comorbidities and complications. AIapplicationsoffergreateraccuracy,efficiency,easeofuse ArtificialIntelligence(AI)andMachineLearning(ML)canbe valuabletoolsforunderstandingandmanagingType2 Diabetes. Infact,it’saboontothepatientsandtheirphysiciansand familymembersandgivesthemcompletesatisfaction.Byconsideringmultipledatasources,AIcanprovideacomprehensive viewofanindividual'shealthstatusandhelpinmakingmoreinformedtreatmentdecisions.
Declarations
Acknowledgements: wearethankfultotheChairmanofMHRCandtotheResearchDirectorfortheirencouragement.
Conflict of Interest:Nonedeclared
Funding:None
References/ further reading
1. NomuraA,NoguchiM,KometaniM,FurukawaK,YonedaT.ArtificialIntelligenceinCurrentDiabetesManagement andPrediction.CurrDiabRep.2021Dec13;21(12):61.
2. Zhang, L., Wang, Y., Niu, M. et al. Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinesepopulation:theHenanRuralCohortStudy.SciRep10,4406(2020).
3. Miotto,R.,Wang,F.,Wang,S.,Jiang,X.,&Dudley,J.T.(2018).Deeplearningforhealthcare:review,opportunitiesand challenges.BriefingsinBioinformatics,19(6),1236–1246.
4. Theerthagiri, P., Ruby, A.U. & Vidya, J. Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms.SNCOMPUT.SCI.4,72(2023).
5. ContrerasI,VehiJ.ArtificialIntelligenceforDiabetesManagementandDecisionSupport: LiteratureReview.JMed InternetRes.2018May30;20(5):e10775.
6. Obermeyer,Z.,&Emanuel,E.J.(2016).PredictingtheFuture BigData,MachineLearning,andClinicalMedicine. NewEnglandJournalofMedicine,375(13),1216–1219.
7. Jamil, K., Asim Uddin, M., & Gade, S. (2023). Leveraging Artificial Intelligence for Targeted Response in Allergic Rhinitis.BritishJournalofHealthcareandMedicalResearch,Vol-10(3).281-283.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 02 | Feb 2024 www.irjet.net p-ISSN:2395-0072
8. Singla R, Singla A, Gupta Y, Kalra S. Artificial Intelligence/Machine Learning in Diabetes Care. Indian J Endocrinol Metab.2019Jul-Aug;23(4):495-497.doi:10.4103/ijem.IJEM_228_19.
9. AbhariS,NiakanKalhoriSR,EbrahimiM,HasannejadaslH,GaravandA.ArtificialIntelligenceApplicationsinType 2 Diabetes Mellitus Care: Focus on Machine Learning Methods. Healthc Inform Res. 2019 Oct;25(4):248-261. doi: 10.4258/hir.2019.25.4.248.Epub2019Oct31.
10. Fregoso-Aparicio,L.,Noguez,J.,Montesinos,L.etal.Machinelearninganddeeplearningpredictivemodelsfortype2 diabetes:asystematicreview.DiabetolMetabSyndr13,148(2021).
11. Makino, M., Yoshimoto, R., Ono, M. et al. Artificial intelligence predicts the progression of diabetic kidney disease usingbigdatamachinelearning.SciRep9,11862(2019).
12. NationalAcademiesofSciences,Engineering,andMedicine.(2018).ArtificialIntelligenceinHealthcare:TheHope, theHype,thePromise,thePeril.REPORTS-doi:10.17226/25438
13. Oikonomakos,I.T.,Steenblock,C.&Bornstein,S.R.Artificialintelligenceindiabetesmellitusand endocrinediseases whatcanweexpect?.NatRevEndocrinol19,375–376(2023).
14. Topol, E. J. (2019). High-Performance Medicine: The Convergence of Human and Artificial Intelligence. Nature Medicine,25(1),44–56.
15. Ellahham S. Artificial Intelligence: The Future for Diabetes Care. Am J Med. 2020 Aug;133(8):895-900. doi: 10.1016/j.amjmed.2020.03.033.Epub2020Apr20
16. Dankwa-MullanI,RivoM,Sepulveda M,Park Y,SnowdonJ,RheeK.TransformingDiabetesCareThroughArtificial Intelligence:TheFutureIsHere.PopulHealthManag.2019Jun;22(3):229-242.doi:10.1089/pop.2018.0129.Epub 2018Oct2.
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