Publication Cover
Automatika
Journal for Control, Measurement, Electronics, Computing and Communications
Volume 65, 2024 - Issue 3
487
Views
0
CrossRef citations to date
0
Altmetric
Regular Paper

A novel framework for multiple disease prediction in telemedicine systems using deep learning

&
Pages 763-777 | Received 16 Sep 2023, Accepted 10 Dec 2023, Published online: 26 Feb 2024

References

  • Wootton R, Craig J, Patterson V. Introduction to telemedicine. United Kingdom: CRC Press; 2017.
  • Lilly CM, Motzkus C, Rincon T, et al. ICU telemedicine program financial outcomes. Chest. 2017;151(2):286–297. doi:10.1016/j.chest.2016.11.029
  • Mehrotra A, Jena AB, Busch AB, et al. Utilization of telemedicine among rural Medicare beneficiaries. Jama. 2016;315(18):2015–2016. doi:10.1001/jama.2016.2186
  • Maheu M, Whitten P, Allen A. eHealth, Telemedicine & Telehealth: A comprehensive guide, New York; 2004.
  • Chowdhury SM, Kabir MH, Ashrafuzzaman K, et al. A telecommunication network architecture for telemedicine in Bangladesh and its applicability. Intern J Digit Content Technol Applic. 2009;3(3):4), doi:10.4156/jdcta.vol3.issue3.20
  • Huang EY, Knight S, Guetter CR, et al. Telemedicine and telementoring in the surgical specialties: A narrative review. Amer J Surg. 2019;218(4):760–766. doi:10.1016/j.amjsurg.2019.07.018
  • Larose DT. Discovering knowledge in data. An introduction to data mining. New Jersey: John Wiley & Sons Publisher; 2005; ISBN 0-471-66657-2.
  • Dash M, Shadangi PY, Muduli K, et al. Predicting the motivators of telemedicine acceptance in COVID-19 pandemic using multiple regression and ANN approach. J Stat Manage Syst. 2021;24(2):319–339. doi:10.1080/09720510.2021.1875570
  • Ahmed ST, Sandhya M, Sankar S. TelMED: dynamic user clustering resource allocation technique for MooM datasets under optimizing telemedicine networks. Wirel Person Commun. 2020;112(2):1061–1077. doi:10.1007/s11277-020-07091-x
  • Sadineni PK. Developing a model to enhance the quality of health informatics using big data. In 2020 fourth international conference on I-SMAC (IoT in social, mobile, analytics and cloud)(I-SMAC) (pp. 1267–1272). IEEE; 2020.
  • Sornalakshmi M, Balamurali S, Venkatesulu M, …Muthu BA. Hybrid method for mining rules based on enhanced Apriori algorithm with sequential minimal optimization in the healthcare industry. Neural Comput Applic. 2020: 1–14.
  • Choi SY, Chung K. Knowledge process of health big data using MapReduce-based associative mining. Pers Ubiquitous Comput. 2020;24:571–581. doi:10.1007/s00779-019-01230-3
  • Priyadarshan DJ, Sanjay KK, Kathiresan S, et al. Patient health monitoring using IoT with machine learning. Intern Res J Eng Technol (IRJET). 2019;6(03.
  • Sandhiya R, Sundarambal M. Clustering of biomedical documents using ontology-based TF-IGM enriched semantic smoothing model for telemedicine applications. Cluster Comput. 2019;22:3213–3230. doi:10.1007/s10586-018-2023-4
  • Anusuya TK, Maharajothi P. A survey of telemedicine services using data mining. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN, 2456–3307; 2019.
  • Thouheed Ahmed S, Sandhya M. Real-time biomedical recursive images detection algorithm for Indian telemedicine environment. In: Cognitive informatics and soft computing: proceeding of CISC 2017. Springer Singapore; 2019. p. 723–731.
  • Sukumar P, Monika G, Gokila D, et al. An NLP based ontology architecture for dealing with Heterogeneous data to telemedicine systems. South Asian J Eng Technol. 2019;8(1):89–92.
  • Sarkar BK, Sana SS. An e-healthcare system for disease prediction using hybrid data mining technique. J Model Manage. 2019;14(3):628–661. doi:10.1108/JM2-05-2018-0069
  • Ahmed MIB. Virtual clinic: A CDSS assisted telemedicine framework. In: Telemedicine technologies. Academic Press; 2019. p. 227–238.
  • Ahmed ST, Sandhya M, Sankar S. An optimized RTSRV machine learning algorithm for biomedical signal transmission and regeneration for a telemedicine environment. Procedia Comput Sci. 2019;152:140–149. doi:10.1016/j.procs.2019.05.036
  • Sandhiya R, Sundarambal M. Chicken swarm optimization-based clustering of biomedical documents and health records to improve telemedicine applications. Intern J Enterpr Netw Manage. 2019;10(3-4):305–328. doi:10.1504/IJENM.2019.103158
  • Peral J, Ferrández A, Gil D, et al. An ontology-oriented architecture for dealing with heterogeneous data applied to telemedicine systems. IEEE Access. 2018;6:41118–41138. doi:10.1109/ACCESS.2018.2857499
  • https://github.com/ybifoundation/Dataset/raw/main/MultipleDiseasePrediction.csv
  • Sherstinsky A. Fundamentals of recurrent neural network (RNN) and long shortterm memory (LSTM) network. Physica D: Nonlin Phenomena. 2020;404:132306), doi:10.1016/j.physd.2019.132306
  • https://www.pluralsight.com/guides/introduction-to-lstm-units-in-rnn.
  • Kadum SY, Salman OH, Taha ZK, et al. Machine learning-based telemedicine framework to prioritize remote patients with multi-chronic diseases for emergency healthcare services. Netwo Model Anal Health Inform Bioinform. 2023;12(1):11), doi:10.1007/s13721-022-00407-w
  • Gupta R, SmitaKumari A, Ambasta RK, et al. New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson’s disease. Ageing Res Rev. 2023: 102013), doi:10.1016/j.arr.2023.102013
  • Bhat SS, Banu M, Ansari GA, et al. A risk assessment and prediction framework for diabetes mellitus using machine learning algorithms. Healthc Analyt. 2023: 100273), doi:10.1016/j.health.2023.100273
  • Chakraborty C, Gupta B, Ghosh SK. A review on telemedicine-based WBAN framework for patient monitoring. Telemed e-Health. 2013;19(8):619–626. doi:10.1089/tmj.2012.0215
  • Chakraborty C, Gupta B, Ghosh SK. Mobile metadata assisted community database of chronic wound images. Wound Med. 2014;6:34–42. doi:10.1016/j.wndm.2014.09.002
  • Chakraborty C. Mobile health (m-Health) for tele-wound monitoring: Role of m-Health in wound management. In: Mobile health applications for quality healthcare delivery. IGI Global; 2019. p. 98–116.
  • Verma S, RishabhaMalviya M, Tripathi BD. Tele-health monitoring using artificial intelligence deep learning framework. In: Deep learning for targeted treatments: transformation in healthcare; 2022. p. 199–228.
  • Kadu A, Singh M, Ogudo K. A novel scheme for classification of epilepsy using machine learning and a fuzzy inference system based on wearable-sensor health parameters. Sustainability. 2022;14(22):15079. doi:10.3390/su142215079