This paper is in the following e-collection/theme issue:

Digital Health Reviews (1326) Artificial Intelligence (1421) Machine Learning (1501) Diabetes Self-Management (495) Glucose Tracking and Self-Monitoring of Blood Glucose (166)

Published on in Vol 21, No 5 (2019): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11030, first published .
Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Authors of this article:

Ashenafi Zebene Woldaregay1 Author Orcid Image ;   Eirik Årsand2 Author Orcid Image ;   Taxiarchis Botsis3 Author Orcid Image ;   David Albers4 Author Orcid Image ;   Lena Mamykina4 Author Orcid Image ;   Gunnar Hartvigsen1 Author Orcid Image
Article Authors Cited by (109) Tweetations (9) Metrics

Journals

  1. Singla R, Singla A, Gupta Y, Kalra S. Artificial intelligence/machine learning in diabetes care. Indian Journal of Endocrinology and Metabolism 2019;23(4):495 View
  2. Oroojeni Mohammad Javad M, Agboola S, Jethwani K, Zeid A, Kamarthi S. A Reinforcement Learning–Based Method for Management of Type 1 Diabetes: Exploratory Study. JMIR Diabetes 2019;4(3):e12905 View
  3. Woldaregay A, Launonen I, Albers D, Igual J, Årsand E, Hartvigsen G. A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism. Journal of Medical Internet Research 2020;22(8):e18912 View
  4. Seo W, Lee Y, Lee S, Jin S, Park S. A machine-learning approach to predict postprandial hypoglycemia. BMC Medical Informatics and Decision Making 2019;19(1) View
  5. Woldaregay A, Launonen I, Årsand E, Albers D, Holubová A, Hartvigsen G. Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System. Journal of Medical Internet Research 2020;22(8):e18911 View
  6. Elhadd T, Mall R, Bashir M, Palotti J, Fernandez-Luque L, Farooq F, Mohanadi D, Dabbous Z, Malik R, Abou-Samra A. Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast during ramadan (The PROFAST – IT Ramadan study). Diabetes Research and Clinical Practice 2020;169:108388 View
  7. Chaki J, Thillai Ganesh S, Cidham S, Ananda Theertan S. Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review. Journal of King Saud University - Computer and Information Sciences 2022;34(6):3204 View
  8. Zaitcev A, Eissa M, Hui Z, Good T, Elliott J, Benaissa M. A Deep Neural Network Application for Improved Prediction of $\text{HbA}_{\text{1c}}$ in Type 1 Diabetes. IEEE Journal of Biomedical and Health Informatics 2020;24(10):2932 View
  9. Basu S, Johnson K, Berkowitz S. Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes. Current Diabetes Reports 2020;20(12) View
  10. Kodama S, Fujihara K, Shiozaki H, Horikawa C, Yamada M, Sato T, Yaguchi Y, Yamamoto M, Kitazawa M, Iwanaga M, Matsubayashi Y, Sone H. Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis. JMIR Diabetes 2021;6(1):e22458 View
  11. Liu W, Chen J, He L, Cai X, Zhang R, Gong S, Yang X, Wang J, Han X, Shi D, Ji L. Flash glucose monitoring data analysed by detrended fluctuation function on beta‐cell function and diabetes classification. Diabetes, Obesity and Metabolism 2021;23(3):774 View
  12. Dave D, Erraguntla M, Lawley M, DeSalvo D, Haridas B, McKay S, Koh C. Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study. JMIR Diabetes 2021;6(2):e26909 View
  13. Deberneh H, Kim I. Prediction of Type 2 Diabetes Based on Machine Learning Algorithm. International Journal of Environmental Research and Public Health 2021;18(6):3317 View
  14. Veiga R, Schuler-Faccini L, França G, Andrade R, Teixeira M, Costa L, Paixão E, Costa M, Barreto M, Oliveira J, Oliveira W, Cardim L, Rodrigues M. Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation. Scientific Reports 2021;11(1) View
  15. Peeks F, Hoogeveen I, Feldbrugge R, Burghard R, de Boer F, Fokkert‐Wilts M, van der Klauw M, Oosterveer M, Derks T. A retrospective in‐depth analysis of continuous glucose monitoring datasets for patients with hepatic glycogen storage disease: Recommended outcome parameters for glucose management. Journal of Inherited Metabolic Disease 2021;44(5):1136 View
  16. Bent B, Cho P, Henriquez M, Wittmann A, Thacker C, Feinglos M, Crowley M, Dunn J. Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches. npj Digital Medicine 2021;4(1) View
  17. Liu Y, Liu W, Chen H, Cai X, Zhang R, An Z, Shi D, Ji L. Graph Convolutional Network Enabled Two-Stream Learning Architecture for Diabetes Classification based on Flash Glucose Monitoring Data. Biomedical Signal Processing and Control 2021;69:102896 View
  18. Zhang M, Flores K, Tran H. Deep learning and regression approaches to forecasting blood glucose levels for type 1 diabetes. Biomedical Signal Processing and Control 2021;69:102923 View
  19. Khatoon F, Ali S, Kumar V, Elasbali A, Alhassan H, Alharethi S, Islam A, Hassan M. Pharmacological features, health benefits and clinical implications of honokiol. Journal of Biomolecular Structure and Dynamics 2023;41(15):7511 View
  20. Nemat H, Khadem H, Eissa M, Elliott J, Benaissa M. Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning Approach. IEEE Journal of Biomedical and Health Informatics 2022;26(6):2758 View
  21. Rossi A, Venema A, Haarsma P, Feldbrugge L, Burghard R, Rodriguez-Buritica D, Parenti G, Oosterveer M, Derks T. A Prospective Study on Continuous Glucose Monitoring in Glycogen Storage Disease Type Ia: Toward Glycemic Targets. The Journal of Clinical Endocrinology & Metabolism 2022;107(9):e3612 View
  22. Galland J. L’intelligence artificielle va révolutionner la médecine interne. La Revue de Médecine Interne 2022;43(5):275 View
  23. Nemat H, Khadem H, Elliott J, Benaissa M. Causality analysis in type 1 diabetes mellitus with application to blood glucose level prediction. Computers in Biology and Medicine 2023;153:106535 View
  24. Keleko A, Kamsu-Foguem B, Ngouna R, Tongne A. Health condition monitoring of a complex hydraulic system using Deep Neural Network and DeepSHAP explainable XAI. Advances in Engineering Software 2023;175:103339 View
  25. Al-Naib I. Terahertz Asymmetric S-Shaped Complementary Metasurface Biosensor for Glucose Concentration. Biosensors 2022;12(8):609 View
  26. Fleischer J, Hansen T, Cichosz S. Hypoglycemia event prediction from CGM using ensemble learning. Frontiers in Clinical Diabetes and Healthcare 2022;3 View
  27. Allam A, Feuerriegel S, Rebhan M, Krauthammer M. Analyzing Patient Trajectories With Artificial Intelligence. Journal of Medical Internet Research 2021;23(12):e29812 View
  28. Kumar Y, Koul A, Singla R, Ijaz M. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of Ambient Intelligence and Humanized Computing 2023;14(7):8459 View
  29. D'Antoni F, Petrosino L, Marchetti A, Bacco L, Pieralice S, Vollero L, Pozzilli P, Piemonte V, Merone M. Layered Meta-Learning Algorithm for Predicting Adverse Events in Type 1 Diabetes. IEEE Access 2023;11:9074 View
  30. Zhu T, Uduku C, Li K, Herrero P, Oliver N, Georgiou P. Enhancing self-management in type 1 diabetes with wearables and deep learning. npj Digital Medicine 2022;5(1) View
  31. D’Antoni F, Petrosino L, Sgarro F, Pagano A, Vollero L, Piemonte V, Merone M. Prediction of Glucose Concentration in Children with Type 1 Diabetes Using Neural Networks: An Edge Computing Application. Bioengineering 2022;9(5):183 View
  32. Aloraynan A, Rassel S, Xu C, Ban D. A Single Wavelength Mid-Infrared Photoacoustic Spectroscopy for Noninvasive Glucose Detection Using Machine Learning. Biosensors 2022;12(3):166 View
  33. Ossai C, Wickramasinghe N. Automatic user sentiments extraction from diabetes mobile apps – An evaluation of reviews with machine learning. Informatics for Health and Social Care 2023;48(3):211 View
  34. Alqahtani A, Roy A. Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents. Evidence-Based Complementary and Alternative Medicine 2022;2022:1 View
  35. Nimri R, Phillip M, Kovatchev B. Decision Support Systems and Closed‐Loop. Diabetes Technology & Therapeutics 2022;24(S1):S-58 View
  36. Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi M. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetology & Metabolic Syndrome 2022;14(1) View
  37. Pragathi P, Nagaraja Rao A. An effective integrated machine learning approach for detecting diabetic retinopathy. Open Computer Science 2022;12(1):83 View
  38. Lobo B, Farhy L, Shafiei M, Kovatchev B. A Data-Driven Approach to Classifying Daily Continuous Glucose Monitoring (CGM) Time Series. IEEE Transactions on Biomedical Engineering 2022;69(2):654 View
  39. Hodgson S, Cheema S, Rana Z, Olaniyan D, O’Leary E, Price H, Dambha‐Miller H. Population stratification in type 2 diabetes mellitus: A systematic review. Diabetic Medicine 2022;39(1) View
  40. Dénes-Fazakas L, Siket M, Szilágyi L, Kovács L, Eigner G. Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals. Sensors 2022;22(21):8568 View
  41. Xiuli P, Quanzhong L, Yannian W, Dengfeng Y. High-precision blood glucose prediction and hypoglycemia warning based on the LSTM-GRU model. Trends in Computer Science and Information Technology 2022;7(3):074 View
  42. Andellini M, Haleem S, Angelini M, Ritrovato M, Schiaffini R, Iadanza E, Pecchia L. Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population: study protocol. Health and Technology 2023;13(1):145 View
  43. Banerjee S, Slaughter G. Flexible battery-less wireless glucose monitoring system. Scientific Reports 2022;12(1) View
  44. Lim M, Cho Y, Kim S. Multi-Task Disentangled Autoencoder for Time-Series Data in Glucose Dynamics. IEEE Journal of Biomedical and Health Informatics 2022;26(9):4702 View
  45. Stawarz K, Katz D, Ayobi A, Marshall P, Yamagata T, Santos-Rodriguez R, Flach P, O’Kane A. Co-designing opportunities for Human-Centred Machine Learning in supporting Type 1 diabetes decision-making. International Journal of Human-Computer Studies 2023;173:103003 View
  46. Gautier T, Ziegler L, Gerber M, Campos-Náñez E, Patek S. Artificial intelligence and diabetes technology: A review. Metabolism 2021;124:154872 View
  47. Zaizar-Fregoso S, Lara-Esqueda A, Hernández-Suarez C, Delgado-Enciso J, Garcia-Nevares A, Canseco-Avila L, Guzman-Esquivel J, Rodriguez-Sanchez I, Martinez-Fierro M, Ceja-Espiritu G, Ochoa-Díaz-Lopez H, Espinoza-Gomez F, Sanchez-Diaz I, Delgado-Enciso I, Jia G. Using Artificial Intelligence to Develop a Multivariate Model with a Machine Learning Model to Predict Complications in Mexican Diabetic Patients without Arterial Hypertension (National Nested Case-Control Study): Metformin and Elevated Normal Blood Pressure Are Risk Factors, and Obesity Is Protective. Journal of Diabetes Research 2023;2023:1 View
  48. Ye H, Meng Y. Honokiol regulates endoplasmic reticulum stress by promoting the activation of the sirtuin 1-mediated protein kinase B pathway and ameliorates high glucose/high fat-induced dysfunction in human umbilical vein endothelial cells. Endocrine Journal 2021;68(8):981 View
  49. Al-Naib I. Sensing Glucose Concentration Using Symmetric Metasurfaces under Oblique Incident Terahertz Waves. Crystals 2021;11(12):1578 View
  50. De Falco I, Della Cioppa A, Koutny T, Ubl M, Krcma M, Scafuri U, Tarantino E. A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction. Sensors 2023;23(6):2957 View
  51. Khadem H, Nemat H, Elliott J, Benaissa M. Blood Glucose Level Time Series Forecasting: Nested Deep Ensemble Learning Lag Fusion. Bioengineering 2023;10(4):487 View
  52. Zaitcev A, Eissa M, Hui Z, Good T, Elliott J, Benaissa M. Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study. Frontiers in Clinical Diabetes and Healthcare 2023;4 View
  53. Arora S, Kumar S, Kumar P. Multivariate Models of Blood Glucose Prediction in Type1 Diabetes: A Survey of the State-of-the-art. Current Pharmaceutical Biotechnology 2023;24(4):532 View
  54. Chen Q, Shi T, Du D, Wang B, Zhao S, Gao Y, Wang S, Zhang Z. Non-destructive diagnostic testing of cardiac myxoma by serum confocal Raman microspectroscopy combined with multivariate analysis. Analytical Methods 2023;15(21):2578 View
  55. Afentakis I, Unsworth R, Herrero P, Oliver N, Reddy M, Georgiou P. Development and Validation of Binary Classifiers to Predict Nocturnal Hypoglycemia in Adults With Type 1 Diabetes. Journal of Diabetes Science and Technology 2023 View
  56. Uymaz P, Uymaz A, Akgül Y. Assessing the Behavioral Intention of Individuals to Use an AI Doctor at the Primary, Secondary, and Tertiary Care Levels. International Journal of Human–Computer Interaction 2024;40(18):5229 View
  57. Rodríguez-Rodríguez I, Campo-Valera M, Rodríguez J. Forecasting glycaemia for type 1 diabetes mellitus patients by means of IoMT devices. Internet of Things 2023;24:100945 View
  58. Arias J, Ramos M, Cubillas J. Predicting emergency health care demands due to respiratory diseases. International Journal of Medical Informatics 2023;177:105163 View
  59. Wen S, Li H, Tao R. A 2-dimensional model framework for blood glucose prediction based on iterative learning control architecture. Medical & Biological Engineering & Computing 2023;61(10):2593 View
  60. Cai D, Wu W, Cescon M, Liu W, Ji L, Shi D. Data-enabled learning and control algorithms for intelligent glucose management: The state of the art. Annual Reviews in Control 2023;56:100897 View
  61. Husain K, Sarhan S, AlKhalifa H, Buhasan A, Moin A, Butler A. Dementia in Diabetes: The Role of Hypoglycemia. International Journal of Molecular Sciences 2023;24(12):9846 View
  62. Huang S, Liang Y, Li J, Li X. Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review. Journal of Medical Internet Research 2023;25:e51024 View
  63. Hadjisolomou E, Antoniadis K, Rousou M, Vasiliades L, Abu-Alhaija R, Herodotou H, Michaelides M, Kyriakides I, Zervas E. Predicting Coastal Dissolved Inorganic Nitrogen Levels by Applying Data-Driven Modelling: The Case Study of Cyprus (Eastern Mediterranean Sea). E3S Web of Conferences 2023;436:10002 View
  64. Sharma I, Nguyen T, Singh S, Ongwere T. Predicting an Optimal Medication/Prescription Regimen for Patient Discordant Chronic Comorbidities Using Multi-Output Models. Information 2024;15(1):31 View
  65. Scimone A, Eckelt K, Streit M, Hinterreiter A. Marjorie: Visualizing Type 1 Diabetes Data to Support Pattern Exploration. IEEE Transactions on Visualization and Computer Graphics 2023:1 View
  66. Lu H, Ding X, Hirst J, Yang Y, Yang J, Mackillop L, Clifton D. Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes. IEEE Reviews in Biomedical Engineering 2024;17:98 View
  67. Jacobs P, Herrero P, Facchinetti A, Vehi J, Kovatchev B, Breton M, Cinar A, Nikita K, Doyle F, Bondia J, Battelino T, Castle J, Zarkogianni K, Narayan R, Mosquera-Lopez C. Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities. IEEE Reviews in Biomedical Engineering 2024;17:19 View
  68. Seo W, Kim N, Park S, Jin S, Park S. Generative adversarial network-based data augmentation for improving hypoglycemia prediction: A proof-of-concept study. Biomedical Signal Processing and Control 2024;92:106077 View
  69. Chowdhury M, Chowdhury M, Alqahtani A. MMG-net: Multi modal approach to estimate blood glucose using multi-stream and cross modality attention. Biomedical Signal Processing and Control 2024;92:105975 View
  70. Haleem M, Cisuelo O, Andellini M, Castaldo R, Angelini M, Ritrovato M, Schiaffini R, Franzese M, Pecchia L. A Self-Attention Deep Neural Network Regressor for real time blood glucose estimation in paediatric population using physiological signals. Biomedical Signal Processing and Control 2024;92:106065 View
  71. Cichosz S, Hejlesen O, Jensen M. Identification of individuals with diabetes who are eligible for continuous glucose monitoring forecasting. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2024;18(2):102972 View
  72. Eghbali-Zarch M, Masoud S. Application of machine learning in affordable and accessible insulin management for type 1 and 2 diabetes: A comprehensive review. Artificial Intelligence in Medicine 2024;151:102868 View
  73. Dénes-Fazakas L, Simon B, Hartvég Á, Kovács L, Dulf É, Szilágyi L, Eigner G. Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks. Sensors 2024;24(8):2412 View
  74. Li N, Qi Y, Li C, Zhao Z. Active Learning for Data Quality Control: A Survey. Journal of Data and Information Quality 2024;16(2):1 View
  75. Matboli M, Al-Amodi H, Khaled A, Khaled R, Roushdy M, Ali M, Diab G, Elnagar M, Elmansy R, TAhmed H, Ahmed E, Elzoghby D, M.Kamel H, Farag M, ELsawi H, Farid L, Abouelkhair M, Habib E, Fikry H, Saleh L, Aboughaleb I. Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats. Frontiers in Endocrinology 2024;15 View
  76. Lebech Cichosz S, Hasselstrøm Jensen M, Schou Olesen S. Development and Validation of a Machine Learning Model to Predict Weekly Risk of Hypoglycemia in Patients with Type 1 Diabetes Based on Continuous Glucose Monitoring. Diabetes Technology & Therapeutics 2024;26(7):457 View
  77. Aden D, Zaheer S, Khan S. Possible benefits, challenges, pitfalls, and future perspective of using ChatGPT in pathology. Revista Española de Patología 2024;57(3):198 View
  78. Khater H, Sallabi F, Serhani M, Barka E, Shuaib K, Tariq A, Khayat M. Empowering Healthcare With Cyber-Physical System—A Systematic Literature Review. IEEE Access 2024;12:83952 View
  79. Dave D, Vyas K, Cote G, Erraguntla M. Hypoglycemia and hyperglycemia detection using ECG: A multi-threshold based personalized fusion model. Biomedical Signal Processing and Control 2024;96:106569 View
  80. Maza D, Ojo J, Akinlade G. A predictive machine learning framework for diabetes. Turkish Journal of Engineering 2024;8(3):583 View
  81. Torrik A, Zarif M. Machine learning assisted sorting of active microswimmers. The Journal of Chemical Physics 2024;161(9) View
  82. Nemat H, Khadem H, Elliott J, Benaissa M. Data-driven blood glucose level prediction in type 1 diabetes: a comprehensive comparative analysis. Scientific Reports 2024;14(1) View
  83. Coskun A. Diagnosis Based on Population Data versus Personalized Data: The Evolving Paradigm in Laboratory Medicine. Diagnostics 2024;14(19):2135 View
  84. Chan P, Jin E, Jansson M, Chew H. AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review. Journal of Medical Internet Research 2024;26:e58892 View
  85. Cichosz S, Olesen S, Jensen M. Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients With Type 1 Diabetes Based on Continuous Glucose Monitoring. Journal of Diabetes Science and Technology 2024 View
  86. Zhang Z, Ji M, Zhao Q, Jiang L, Fan S, Zuo H. Predictive value of glucose coefficient of variation for in-hospital mortality in acute myocardial infarction patients undergoing PCI: Insights from the MIMIC-IV database. International Journal of Cardiology Cardiovascular Risk and Prevention 2024;23:200347 View
  87. Chushig-Muzo D, Calero-Díaz H, Fabelo H, Årsand E, van Dijk P, Soguero-Ruiz C. Characterizing the Impact of Physical Activity on Patients with Type 1 Diabetes Using Statistical and Machine Learning Models. Applied Sciences 2024;14(21):9870 View
  88. Allan-Blitz L, Ambepitiya S, Prathapa J, Rietmeijer C, Kularathne Y, Klausner J. Synergistic pairing of synthetic image generation with disease classification modeling permits rapid digital classification tool development. Scientific Reports 2024;14(1) View
  89. Basile I, Sannino G. Blood glucose level prediction in type 1 diabetes: A comparative analysis of interpretable artificial intelligence approaches. Results in Engineering 2025;25:103681 View
  90. Yu X, Yang Z, Wang X, Sun X, Shen R, Li H, Zhang M. A prior-knowledge-guided dynamic attention mechanism to predict nocturnal hypoglycemic events in type 1 diabetes. BMC Medical Informatics and Decision Making 2024;24(1) View

Books/Policy Documents

  1. Ardabili S, Mosavi A, Várkonyi-Kóczy A. Engineering for Sustainable Future. View
  2. Ulapane N, Wickramasinghe N. Optimizing Health Monitoring Systems With Wireless Technology. View
  3. Weatherall J, Khan F, Patel M, Dearden R, Shameer K, Dennis G, Feldberg G, White T, Khosla S. The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry. View
  4. Chauhan R, Kaur H, Alankar B. Meta Heuristic Techniques in Software Engineering and Its Applications. View
  5. Ghosh S, Dasgupta R. Machine Learning in Biological Sciences. View
  6. Kinzel C, Pfannstiel M. Künstliche Intelligenz im Gesundheitswesen. View
  7. Dhanapal A, Sylvia Subapriya M. , Subramaniam K, Appukutty M. Integrating AI in IoT Analytics on the Cloud for Healthcare Applications. View
  8. Karwasra R, Sharma S, Sharma I, Sharma S. Artificial Intelligence and Autoimmune Diseases. View
  9. Kaur I, Ali A. Fog Computing for Intelligent Cloud IoT Systems. View
  10. Raj S, Agarwal A, Tripathi S, Gupta N. Prediction in Medicine: The Impact of Machine Learning on Healthcare. View